Patent Publication Number: US-11641804-B2

Title: Bale detection and classification using stereo cameras

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. non-provisional application Ser. No. 16/302,427, filed Nov. 16, 2018, which is a national stage entry of PCT/IB2017/000665, filed May 16, 2017, which claims priority to provisional application Ser. No. 62/338,781, filed May 19, 2016, all of which are incorporated herein by reference in their entirety. 
    
    
     FIELD OF THE INVENTION 
     The present disclosure is directed to systems and methods for detecting bales of material situated on the ground using a sensor system and, more particularly, to systems and methods for detecting the position and orientation of such bales using stereo cameras. The present disclosure is also directed to an autonomous bale mover configured to automatically detect bales within a specified region using a sensor system, confirm that detected bales are actual bales, pick up individual bales, and transport bales to a specified stacking location. 
     BACKGROUND OF THE INVENTION 
     Moving bales of material from a field is a tedious, relatively labor intensive, process that is currently done with a machine, typically a tractor towing a bale mover, or a tractor with a loader, that is controlled by an operator. Recent development of conversion technology is enabling construction of commercial scale systems designed to convert biomass, in the form of agricultural residue, to energy. In this relatively new use, the harvesting process for the agricultural residues introduces new challenges. For example, the process of harvesting and transporting bales of material that was previously left on the ground creates a new labor requirement, when available labor is already scarce. This is combined with the fact that in this situation the amount of time that the bales can be left in the field, before they are moved off the field, is in many situations limited, due to the farmer&#39;s need to completer other post-harvest processes, such as tillage and/or fertilizer application. Also, it will be more difficult in the future to find qualified operators that can operate the machines used for moving bales, thus automating this process, to allow machines to run without an operator, will minimize this issue. 
     Other forms of forage harvesting have similar considerations. For example, when harvesting wet materials that will be ensiled, there is a limited window of time to pick-up and wrap the bales, after they have been baled, to prevent spoilage and dry-matter loss. In this situation, it would also be beneficial to have an autonomous machine that could load the bales off the field and move them from the location where they were deposited by a baler, to a storage position, for wrapping. In these instances, there exists a need for a system that will increase the speed with which bales can be removed from a field, in the context of having limited availability to labor. 
     SUMMARY OF THE INVENTION 
     Embodiments are directed to systems and methods for detecting bales of material situated on the ground using a sensor system. Embodiments are also directed to an autonomous bale mover configured to automatically detect bales within a specified region using a sensor system, confirm that detected bales are actual bales, pick up individual bales, and transport bales to a specified stacking location. 
     According to some embodiments, a method comprises scanning a region of land using a sensor comprising stereo cameras, and producing, by the sensor, an image and disparity data for the image. The method also comprises searching for a vertical object within the image using the disparity data, and determining whether the vertical object is a bale of material using the image. The method further comprises computing an orientation of the bale relative to the sensor using the disparity data. 
     According to other embodiments, an apparatus comprises a sensor comprising a left camera and a right camera. A processor is coupled to the sensor. The processor is configured to produce an image and disparity data for the image, and search for a vertical object within the image using the disparity data. The processor is also configured to determine whether the vertical object is a bale of material using the image, and compute an orientation of the bale relative to the sensor using the disparity data. 
     Other embodiments are directed to an autonomous bale mover comprising an integral power system, a ground-drive system, a bale loading system, and a bale carrying system. The autonomous bale mover also comprises a control system capable of providing control signals for the ground-drive system to control the speed of and direction of travel of the bale mover and to control operation of the bale loading system and the bale carrying system. The autonomous bale mover further comprises a sensor system for detecting the position and orientation of bales. The sensor system may be of a type described hereinabove. 
     Further embodiments are directed to a method implemented with use of an autonomous bale mover of a type described hereinabove. The method comprises defining a region within which the autonomous bale mover operates, and locating bales distributed within the region by the bale mover as the bale mover moves through the region. The method also comprises picking up located bales by the bale mover without stopping, and transporting picked-up bales to a predetermined stacking location within the region by the bale mover. The method further comprises continuing to locate and pick up bales within the region by the bale mover until all bales within the region are transported to the predetermined stacking location. 
     The above summary of the present invention is not intended to describe each embodiment or every implementation of the present invention. Advantages and attainments, together with a more complete understanding of the invention, will become apparent and appreciated by referring to the following detailed description and claims taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a block diagram of a system for detecting and mapping bales of material situated on the ground within a region of land in accordance with various embodiments; 
         FIG.  2    is a block diagram of a system for detecting and mapping bales of material situated on the ground within a region of land in accordance with various embodiments; 
         FIG.  3    illustrates an autonomous bale mover equipped with a stereo camera system and proximity sensors in accordance with various embodiments; 
         FIG.  4    illustrates a method for detecting a bale of material on the ground using a stereo camera system in accordance with various embodiments; 
         FIG.  5    illustrates additional details of the vertical object detection and classification processes shown in  FIG.  4   ; 
         FIGS.  6 A and  6 B  illustrate a sliding window technique for detecting a bale present in an image in accordance with various embodiments; 
         FIG.  7    illustrates a method of estimating bale pose in accordance with various embodiments; 
         FIG.  8 A  illustrates a representative bale of material having a cylindrical side and circular faces which are subject to detection and mapping using a stereo camera system in accordance with various embodiments; 
         FIG.  8 B  shows results of determining which line represents the face of the bale and which line represents the side of the bale using disparity variation for the best fit lines in accordance with various embodiments; 
         FIG.  9    shows a representative bale of material having a cylindrical side and circular faces which are subject to detection and mapping using a stereo camera system in accordance with various embodiments, including generating best fit lines through points in the X-plane and the Z-plane, respectively; 
         FIG.  10    illustrates a method for mapping bales detected by the stereo camera system in a mapping module in accordance with various embodiments; 
         FIG.  11    illustrates a method for updating the mapping module (e.g., world model) in accordance with various embodiments; 
         FIGS.  12 - 18    are different views of an autonomous bale mover in accordance with various embodiments; 
         FIG.  19    is a block diagram of a control system for controlling an autonomous bale mover in accordance with various embodiments; 
         FIG.  20    is a flow chart illustrating various operations of an autonomous bale mover in accordance with various embodiments; 
         FIG.  21    illustrates an autonomous bale mover moving through a field and picking up bales in accordance with various embodiments; 
         FIG.  22    is a three-dimensional graph showing the time to collect bales versus machine capacity and travel speed in accordance with various embodiments; and 
         FIG.  23    illustrates a hydraulic system for an autonomous bale mover in accordance with various embodiments. 
     
    
    
     While the invention is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail herein. It is to be understood, however, that the intention is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims. 
     DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS 
     In the following description of the illustrated embodiments, references are made to the accompanying drawings forming a part hereof, and in which are shown by way of illustration, various embodiments by which the invention may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional changes may be made without departing from the scope of the present invention. 
     Systems, devices or methods according to the present invention may include one or more of the features, structures, methods, or combinations thereof described herein. For example, a device or system may be implemented to include one or more of the advantageous features and/or processes described below. It is intended that such a device, system or method need not include all of the features described herein, but may be implemented to include selected features that provide for useful structures, systems, and/or functionality. 
     System Overview 
       FIG.  1    illustrates a block diagram of a system for detecting and mapping bales of material situated on the ground within a region of land in accordance with various embodiments. The system shown in  FIG.  1    includes a sensor  102 . The sensor  102  includes a stereo camera system comprising a first camera  102   a  and a second camera  102   b . The sensor  102  also includes hardware and software for operating and calibrating the stereo camera system. In  FIG.  1   , the region of land (e.g., a field) includes a first bale  122   a  and a second bale  122   b  situated on the ground  120 . The sensor  102  is shown scanning the region of land in order to detect the bales  122   a  and  122   b , confirm that the detected bales are indeed actual bales, and map their pose (e.g., orientation, but may also include both orientation and position). A sensor processor  104  is coupled to the sensor  102  and to a detector  106 . The sensor processor  104  and detector  106  cooperate to analyze sensor images and disparity data. The detector  106  is configured to implement feature extraction and classification algorithms to detect potential bales (bale observations) and to confirm that potential bales are actual bales. The poses of actual bales produced by the detector  106  are received by a mapping module  102  (also referred to herein as a world model), which incorporates the bale poses in a map of the region being subject to scanning. 
     According to some embodiments, the sensor system (blocks  102 ,  104 ,  106 , and  108 ) shown in  FIG.  1    is an integral component of an autonomous bale moving vehicle, referred to herein as a bale mover. A bale mover has a single intended utility of moving bales that are located throughout a field into a bale storage area located in or near the field. Although the sensor system shown in  FIG.  1    is described in connection with an autonomous bale mover, it is understood that the sensor system and methodology disclosed herein can be used in connection with other machines. It is noted that blocks  102 ,  104 , and  106  are components of a perception unit, which is discussed in greater detail hereinbelow (see, e.g.,  FIG.  19   ). 
     The autonomous bale mover is configured to automatically (without human intervention) detect bales within a specified region using the sensor system, confirm that detected bales (bale observations) are actual bales, pick up individual bales, and transport bales to a specified stacking location. The bale mover includes a bale mover controller  110  which is communicatively coupled to the sensor processor  104 . The bale mover controller  110  cooperates with the sensor processor  104  to orient the bale mover relative to the face (circular end surface) of a bale as part of a bale pickup operation. After properly orientating itself with the face of the bale, the bale mover picks up the bale and then proceeds to search for another bale in the field. 
     After a maximum number of bales have been picked up, the bale mover is configured to transport the bales to a specified stacking location. Upon arriving at the specified stacking location, the bale mover is configured to unload the bales. After unloading the bales, the bale mover may then return to the field to pick up more bales. Embodiments of the disclosure are directed to operating on round bales. It is understood that embodiments of the disclosure contemplate detecting bales of a different shape (e.g., square or rectangular bales). Examples of material contained within a bale include hay or corn, among other agricultural materials. Representative bales are described in U.S. Pat. No. 7,401,547, which is incorporated herein by reference. 
     System Components 
       FIG.  2    is a block diagram of a system for detecting and mapping bales of material situated on the ground within a region of land in accordance with various embodiments. The system shown in  FIG.  2    includes a sensor  102  which incorporates a stereo camera system comprising a first camera  102   a  and a second camera  102   b . The sensor  102  is coupled to a sensor processor  104  and a sensor calibration module  202 . The sensor processor  104  and sensor calibration module  202  cooperate to calibrate the stereo camera system of the sensor  102 . For example, calibration of the stereo camera system typically involves estimating the intrinsic (internal lens distortion, focal length, central coordinate, valid pixels) and extrinsic (external translation and rotation between cameras) camera parameters, such that their images can be rectified and the images appear to be parallel to each other. 
     According to various embodiments, the sensor calibration module  202  uses various function calls defined in an OpenCV function library  204  to generate all of the camera calibration parameters. It is noted that any physical changes in the pair of cameras will require their calibration to be regenerated. OPenCV refers to Open Source Computer Vision, which is a library of programming functions (&gt;500 functions) that support real-time computer vision, including extracting and processing meaningful data from images. The OpenCV library is cross-platform and free to use under an open-source BSD (Berkeley Software Distribution) license. Selected functions of the OpenCV library are stored in the OpenCV function library  204  and accessible by the sensor processor  104  and sensor calibration module  202 . 
     The sensor processor  104  cooperates with the sensor  102  to produce an image  220  and a corresponding disparity map  230  for the image  220 . In  FIG.  2   , the image  220  represents a single frame in which two bales  122   a  and  122   b  (and the ground  123  and sky) are captured in the frame. The disparity map  230  corresponds pixel-by-pixel with the image  230  and provides disparity data for each pixel. Disparity refers to the difference in image location of an object seen by the left and the right cameras due to the horizontal separation between the two cameras. More specifically, the disparity of features between two stereo images can be computed as a shift to the left of an image feature when viewed in the right image. The disparity at a given location in the right image is measured in terms of pixels relative to the left image. Disparity and distance from the cameras are inversely related. As the distance from the cameras increases, the disparity decreases. This relationship allows for depth perception in stereo images. Using known techniques, the points that appear in the 2-D disparity image can be mapped as coordinates in 3-D space. 
     Referring to the sensor  102 , the left camera  102   a  generates a left image and the right camera  102   b  generates a right image. The sensor processor  104  rectifies the left and right images, which rotates the two images and places them on the same plane. The left and right images can be scaled so that the image frames are the same size. Skew adjustments can also be made to make the image pixel rows directly lineup with each other. According to some embodiments, the sensor processor  104  uses various StereoRectify algorithms in the OpenCV function library  204  to perform the rectification transformation. 
     Using the rectified left and right images, the sensor processor  104  generates the disparity map  230  for the image  220 . The sensor processor  104  generates the disparity map (also referred to as a disparity image or disparity data) using the calibration parameters provided by the sensor calibration module  202 . According to some embodiments, the sensor processor  104  uses the Block Matching algorithm in the OpenCV function library  204  to compute the disparity map, shown generally as a disparity image function  210 . As a further step, the sensor processor  104  generates a 3-D point cloud reconstruction of the disparity map. According to some embodiments, the sensor processor  104  uses ReprojectImageTo3D in the OpenCV function library  204  and associated arguments to generate a 3-D point cloud from the disparity map. 
     After generation of the disparity map  230 , a detector  106  operates on the image  220  and disparity map  230  to distinguish the bales  122   a  and  122   b  from other objects in the image  220 . The detector  106  can be implemented to perform feature extraction  212  and object classification  214  in order to make bail observations and detect the bales  122   a  and  122   b  as actual bales. The pose of each of the detected bales  122   a  and  122   b  can be computed by the sensor processor  104  and stored in a mapping module  108 . 
     As was discussed previously, the sensor system shown in  FIG.  2    can be integral to an autonomous bale mover configured to pick up detected bales and transport them to a specified stacking location. In such embodiments, a bale mover controller  110  is coupled to the sensor processor  104  and controls various functions of the bale mover. The bale mover may also include one or more proximity sensors  114  which are also coupled to the bale mover controller  110 . The proximity sensors  114 , such as LIDAR (e.g., 2-D laser scanner) or ultrasonic sensors, are configured to sense for objects (moving or stationary) in proximity to the bale mover machine during operation. In operation, the bale mover controller  110  can cause the bale mover to reduce speed or stop in response to signals received from the proximity sensors  114  (e.g., due to presence of a vehicle or a person). It is noted that in some embodiments, in addition to helping avoid running into objects in the immediate vicinity, the proximity sensors  114  can be used to help identify and locate bales and to generate a map of objects not to run into, then plan paths around such objects. 
       FIG.  3    illustrates an autonomous bale mover  300  equipped with a stereo camera system (sensor  102 ) and proximity sensors  114 . In the embodiment shown in  FIG.  3   , a first proximity sensor  114   a  is mounted on the left side of the bale mover  300  at the rear of the bale mover&#39;s engine assembly. From this mounting location, the first proximity sensor  114   a  has a field of view of about 270°, which is shown as the combination of detection zones  312  and  316  in  FIG.  3   . As shown in  FIG.  3   , the first proximity sensor  114   a  has a detection zone that extends from a 12:00 position to a 3:00 position moving counterclockwise from the 12:00 position. 
     The second proximity sensor  114   b  is mounted on the right side of the bale mover  300  at an upper rear location. From this mounting location, the second proximity sensor  114   b  has a field of view of about 180°, which is shown as detection zone  314  in  FIG.  3    (noting that zone  316  overlaps the lower half of zone  314 ). As shown in  FIG.  3   , the second proximity sensor  114   b  has a detection zone that extends from a 12:00 position to a 6:00 position moving clockwise from the 12:00 position. 
     The two proximity sensors  114   a  and  114   b  provide for a full 360° of proximity detection around the bale mover  300 , with an overlapping zone  316  (between 3:00 and 6:00 positions) located behind and to the right side of the bale mover  300 . The sensor  102  comprising the stereo camera system is shown mounted on the left forward side of the bale mover  300 . The stereo camera system can have a field of view  302  of between about 60° and 100° (e.g., 62°), which allows for a wide scanning area. 
     Bale Detection and Classification 
       FIG.  4    illustrates a method for detecting a bale of material on the ground using a stereo camera system in accordance with various embodiments. It is noted that the processes shown in  FIG.  4    are implemented for each image produced by the stereo camera system, and that the stereo camera system can produce n image frames per second (e.g., n=5-10). The method of  FIG.  4    involves obtaining  402  an image of a landscape using stereo cameras. A disparity map is produced 404 for the image. According to some embodiments, disparity pixels corresponding to the ground in the image are removed  406  from the disparity map. Removing the ground from the disparity map leaves only vertical objects in the landscape, which can significantly increase object detection efficiency. 
     The method of  FIG.  4    further involves scanning the image  408  for a vertical object using a detection window. The detection window has a predetermined size which makes searching for vertical objects more efficient. More particularly, the detection window has a size that corresponds to a size of a bale at a given distance separating a vertical object in the image from the stereo camera system. To accelerate the scanning process, disparity pixels within the detection window are checked to determine if a vertical object is contained within the detection window. A vertical threshold relative to the ground can be established to ignore vertical objects close to the ground that would not be a bale. For example, a typical round bale is 5 feet wide and 6 feet tall. A vertical threshold of 3 feet, for example, can be established, such that only vertical objects greater than the vertical threshold are analyzed. Disparity values for pixels of a vertical object will be noticeably larger than disparity values of pixels for the background (e.g., the sky), which will have little variation in disparity values. A threshold can be empirically determined (e.g., an n pixel shift) for a given vision system arrangement to distinguish disparity pixel values for vertical objects from disparity pixel values of the background. 
     The disparity pixels of the detection window can be quickly analyzed to determine  410  if any disparity pixels have values that exceeds a threshold indicative of a vertical object. If a vertical object is not detected in the detection window, the detection window  413  is moved to a new location within the image and the vertical object detection and classification processes  408 - 410  are repeated. If a vertical object is detected in the detection window, the vertical object is considered a bale observation which requires additional processing to determine if it is an actual bale. The vertical object detected in the detection window is subject to classification  414  in order to determine  416  if the vertical object is an actual bale. If the vertical object is not classified as a bale, the detection window  412  is moved to a new location of the image and the vertical object detection and classification processes  408 - 416  are repeated. 
     If the vertical object is classified as a bale, the image and disparity data of the detection window is saved  418 . Because the detection window may only capture a portion of the vertical object indicative of a bale, a check is made  420  to determine if the end of the bale has been reached. If not, the detection window is advanced  412  to a new location of the image and the vertical object detection and classification processes  408 - 416  are repeated. Repeating this process may result in detection of a different portion of the same bale, in which case the image and disparity data of the detection window covering this different portion of the same bale may be saved  418 . If not at the end of the bale  420 , the detection window is advanced  412  to a new location and the vertical object detection and classification processes  408 - 420  are repeated. If the end of the bale has been reached  420 , all saved detection windows corresponding to the same bale are merged  422  into a single merged window, which concludes  424  the bale detection method shown in  FIG.  4   . The single merged window can be further processed to estimate the pose of the detected bale. 
       FIG.  5    illustrates additional details of the vertical object detection and classification processes  408 - 422  shown in  FIG.  4   . It is noted that the processes shown in  FIG.  5    are implemented for each image produced by the stereo camera system, and that the stereo camera system can produce n image frames per second (e.g., n=5-10). The method shown in  FIG.  5    begins with the assumption that a vertical object has been detected within a detection window and further involves receiving  502  the detection window situated over the image containing the vertical object. The method shown in  FIG.  5    involves two stages of vertical object classification that operate on the image. The first stage of object classification involves extraction  504  of Haar features of the vertical object from the image. Given the detection window containing the vertical object, the Haar features are the sums of the pixel intensities between adjacent rectangular sub-windows (rectangular regions) that are used to calculate the difference between these sums. The Haar features have the capability to adjust the evaluation sub-window size and scale factor of the features. Having extracted the Haar features of the vertical object  504 , the vertical object is classified  506  using the Haar features. 
     According to some embodiments, a support vector machine (SVM) can be used to classify the vertical object using the extracted Haar features. The SVM classifier is a binary classifier which looks for an optimal hyperplane as a decision function. The SVM is trained on images containing a particular object, a bale in this case. The SVM classifier makes decisions regarding the presence of the bale using the extracted Haar features. Object classification  506  can produce a first output (e.g., a binary 1) indicating that the vertical object is likely a bale, and a second output (e.g., a binary 0) indicating that the vertical object is not likely a bale. 
     If the object is not likely a bale  508 , a check is made  510  to determine if the end of the vertical object has been reached. If not, the next window containing the vertical object is processed  512 , and the extraction and classification steps  504  and  506  are repeated for the next window. If the end of the vertical object has been reached  510 , processing returns  514  to bale detection, such as by advancing the detection window to a new location of the image at block  412  of  FIG.  4   . If it is determined  508  that the vertical object is likely a bale, the second stage of vertical object classification commences. 
     The second stage of object classification involves extraction  516  of HOG (Histogram of Oriented Gradients) features of the vertical object. A HOG feature extraction method counts the occurrences of gradient orientations in localized portions of the sub-window (rectangular regions) to create a histogram. The HOG feature extraction method has configurable parameters that are defined specifically to the appearance of the object being evaluated that include the cell size, block size, number and position of blocks, number of orientation sub-divisions, and order of Sobel edge extraction subdivisions. Having extracted the HOG features of the vertical object  516 , the object is classified  518  using the HOG features. According to some embodiments, an SVM can be used to classify the vertical object using the extracted HOG features. Object classification  518  can produce a first output (e.g., a binary 1) indicating that the vertical object is likely a bale, and a second output (e.g., a binary 0) indicating that the vertical object is not likely a bale. 
     If it is determined  520  that the vertical object is not likely a bale, a check is made  510  to determine if the end of the vertical object has been reached. If not, the next window containing the vertical object is processed  512 , and the extraction and classification steps  504 - 518  are repeated for the next window. If the end of the vertical object has been reached  510 , processing returns  514  to bale detection, such as by advancing the detection window to a new location of the image at block  412  of  FIG.  4   . If it is determined  520  that the vertical object is likely a bale, then both stages of the two-stage classification process indicate that the vertical object is likely a bale. As such, the vertical object is deemed  522  an actual bale (e.g., an actual bale has been detected). The image and disparity data for the detection window are saved  524 . 
     As was discussed previously, because the detection window may only capture a portion of the vertical object indicative of a bale, a check is made  526  to determine if the end of the bale has been reached. If not, the next detection window covering the same vertical object in the image is received  502  and the processes from blocks  504 - 526  are repeated. Repeating this process may result in detection of a different portion of the same bale, in which case the image and disparity data of the detection window covering this different portion of the same bale is saved  524 . If the end of the bale has been reached  526 , all saved detection windows corresponding to the same bale are merged  528  into a single merged window, which concludes  530  the bale detection and classification method shown in  FIG.  5   . The single merged window can be further processed to estimate the pose of the detected bale. 
       FIGS.  6 A and  6 B  illustrate a sliding window technique for detecting a bale present in an image in accordance with various embodiments. Referring to  FIG.  6 A , a sliding window  602  is shown sliding across an image which contains a vertical object, which in this case is a bale  604 . As was discussed previously, the detection window  602  has a predetermined size that corresponds to a size of the bale  604  at a given distance separating the bale in the image from the stereo camera system. As such, the size of the detection window  602  is equivalent to that of the bale  604  in the image shown in  FIG.  6 A  (and in  FIG.  6 B ). 
     In scenario A of  FIG.  6 A , the sliding detection window  602  is positioned above and to the left of the vertical object  604 . A check of the disparity pixel values within the detection window  602  in scenario A reveals that no disparity pixel values exceed the threshold, indicating an absence of a vertical object within the detection window  602 . As such, the detection window  602  is advanced to the next location in the image. In this illustrative example, the detection window  602  is advanced along the same row of the image by a distance equivalent to the length of the window  602 . It is noted that the distance of detection window advancement can be greater than or less than the length of the detection window  602 . 
     In scenario B of  FIG.  6 A , the detection window  602  covers a small portion of the vertical object  604 . Although the disparity pixel values within the detection window  602  indicate the presence of a vertical object, the object would not be classified as a bale due to insufficient coverage of the detection window  602  over the vertical object  604 . The image and disparity data contained within the detection window  602  is discarded, and the detection window  602  is advanced to its next location of the image. In scenario C of  FIG.  6 A , the detection window  602  again covers an insufficient portion of the vertical object  604 , leading to the determination that the vertical object contained within the detection window  602  in scenario C is not a bale. The image and disparity data contained within the detection window  602  is discarded, and the detection window  602  is advanced to the next location of the image along the same row. In scenario D, the detection window  602  has moved completely away from the vertical object  604 , leading to the determination that no vertical object is contained within the detection window  602  in scenario D. It can be seen that none of the detection scenarios A-D of  FIG.  6 A  resulted in successful detection of the bale  604 . 
     In  FIG.  6 B , the detection window  602  has been shifted down the image by one or more rows and is shown advancing toward the same bale  604  shown in  FIG.  6 A . In scenario E of  FIG.  6 B , the detection window  602  is positioned slightly above and to the left of the vertical object  604 . A check of the disparity pixel values within the detection window  602  in scenario E reveals that no disparity pixel values exceed the threshold, indicating an absence of a vertical object within the detection window  602 . The detection window  602  is advanced to its next location in the same row, which is shown in scenario F. In scenario F, the detection window  602  covers nearly all of the vertical object  604 . 
     In scenario F, classification of the vertical object  604  results in a positive detection of a bale  604  within the detection window  602 . In general, a positive detection of a vertical object  604  as a bale within a detection window  602  requires a minimum of about 90% coverage of the vertical object  604  by the detection window  602 . In scenario F, this minimum 90% coverage threshold has been satisfied. In response to the positive detection, the image and disparity data contained within the detection window  602  are saved. The detection window  602  is advanced to its next location in the same row, which is shown in scenario G. 
     In scenario G of  FIG.  6 B , the detection window  602  covers an insufficient portion of the vertical object  604 , leading to the determination that the vertical object contained within the detection window  602  in scenario G is not a bale. The image and disparity data contained within the detection window  602  is discarded, and the detection window  602  is advanced to the next location of the image along the same row. In scenario H, the detection window  602  has moved completely away from the vertical object  604 , leading to the determination that no vertical object is contained within the detection window  602  in scenario H. It can be seen in  FIG.  6 B  that one of the detection scenarios, scenario F, resulted in successful detection of the bale  604 . 
     Bale Pose Estimation 
       FIG.  7    illustrates a method of estimating bale pose in accordance with various embodiments. In discussing the method shown in  FIG.  7   , reference will be made to  FIGS.  8 A,  8 B, and  9   . The bale pose estimation method begins with receiving  302  a detection window containing an actual bale. The actual bale detection window can be the merged detection window described in block  520  of  FIG.  5    and block  422  of  FIG.  4   , for example. It is noted that the processes shown in  FIG.  7    are implemented for each detection window that contains a bale. 
     The method shown in  FIG.  7    involves computing  304  3-D points (X, Y, Z) using the disparity map for the detection window. According to some embodiments, the OpenCV function ReprojectImageTo3D and associated arguments can be used to generate a 3-D point cloud from the disparity map. The method also involves computing  306  the position of the bale relative to the sensor using averaged 3-D points. The averaged 3-D points provide a position of the center of the bale. The method further involves projecting  308  X and Z coordinates of the 3-D points to a 2-D (X-Z) plane defining a top-down view of the bale. The method also involves generating  310  best fit lines through the points in the X-plane and the Z-plane, respectively (see, e.g.,  FIG.  9   ). The method further involves determining  312  which line represents the face of the bale (see face  122 - f  in  FIG.  8 A ) and which line represents the side of the bale (see side  122 - s  in  FIG.  8 A ) using disparity variation for the best fit lines. 
     Disparity values for pixels representing the face  122 - f  of the bale  122  differ significantly from the disparity values for pixels representing the side  122 - s  of the bale  122 . The face  122 - f  of the bale  122  is essentially a circular cross-section along a single plane, with relatively little variation in depth along the plane (with some variation due to the baled material). Hence, the disparity values for pixels representing the face  122 - f  of the bale  122  are relatively consistent (e.g., a small variance). In contrast, the side  122 - s  of the bale  122  is a curved cylindrical structure whose depth varies significantly between the top of the bale  122  and the bottom of the bale  122  (e.g., a large variance). As such, the disparity values for pixels representing the side  122 - s  of the bale  122  vary significantly along multiple planes that define the curvature of the cylindrical structure. This unique disparity signature of a round bale is used to determine the orientation of the bale relative to the stereo camera system (sensor). The method involves computing  314  the orientation of the bale relative to the sensor using the line representing the face  122 - f  of the bale  122 , which concludes  316  the bale pose computations of  FIG.  7   . 
       FIG.  8 B  illustrates pose estimation data acquired by the stereo camera system after detecting an actual bale  122 , such as that shown in  FIG.  8 A . As can be seen in  FIG.  8 B , a line  802  corresponding to the face  122 - f  of the bale  122  is drawn through points corresponding to the surface of the generally flat bale face  122 - f . A line  804  corresponding to the side  122 -S of the bale  122  is drawn through points corresponding to the surface of the cylindrical body of the bale  122 . The two lines  802  and  804  are orthogonal to one another, forming an L-shaped signature. As can be seen in  FIG.  8 B , the points in the region of the bale face  122 - f  have a relatively small variance (e.g., width of the white area) relative to the points in the region of the bale side  122 - s . The surface of the bale  122  having the smallest variance in disparity pixel values is considered to be the face  122 - f  of the bale  122 . The surface of the bale  122  with the largest variance in disparity pixel values is considered to be the side  122 - s  of the bale  122 . Distinguishing the face  122 - f  from the side  122 - s  of the bale  122  is important for purposes of aligning an autonomous bale mover with a detected bale in order to pick up the bale. 
     According to some embodiments, the perception unit of the autonomous bale mover looks out about 30 m from the cameras to detect a bale and determine the position of the bale. The pose estimation operates more effectively when closer to the bale. As such, pose estimation is initiated several meters closer to the bale following bale detection. For example, pose estimation can be initiated within about 20 m of the bale subsequent to detecting the bale at about 30 m. The distances for detecting bales and performing pose estimations are configurable, but are generally constrained based on the camera resolution and field of view. It is understood that different cameras can estimate bale pose (and detect bales) at different distances, such as further away than 20 m or 30 m, for example. 
     Bale Mapping 
       FIG.  10    illustrates a method for mapping bales detected by the stereo camera system in a mapping module in accordance with various embodiments. Bale mapping assumes that a sensor system comprising the stereo camera system is mounted on an autonomous bale mover (or other moving platform). Mapping of bales involves transposition of bale poses from one frame of reference to another. More specifically, the stereo camera system has its own frame of reference, referred to as a camera frame, since the camera system can move relative to the center of the autonomous bale mover. The autonomous bale mover has its own frame of reference, referred to as a base frame, and the mapping module (see mapping module  108  in  FIGS.  1  and  2   ) has its own frame of reference, referred to as a map frame. Storing bale poses in the map frame allows the location of bales to be known irrespective of the location of the bale mover. 
     According to various embodiments, the camera, base, and map frames are defined using a Cartesian coordinate system. It is noted, however, that the camera frame is atypical relative to the base frame and the map frame. For the base frame and the map frame, the Z-axis denotes elevation above (or below) the horizontal X-Y plane. For the camera frame, however, image analysis software addresses the pixels of an image as X and Y, and when that image is used to produce 3-D data, the remaining “distance away” axis is the Z-axis. As such, the horizontal plane when referring to pixel analysis is the X-Z plane, while in the base and map frames the horizontal plane is the X-Y plane. In some embodiments, the global position of the bales can be computed, such as by use of a GPS, and stored in the mapping module. It is noted that the global position of the bales is defined in terms of a polar coordinate system. 
       FIG.  10    shows a method of translating bale poses between different frames of reference in accordance with various embodiments. Bale pose measurements are made  1002  by the stereo camera system using the camera frame of reference. Using known techniques, the bale pose measurements are translated  1004  from the camera frame to the base frame of the autonomous bale mover  1006 . The bale pose measurements are then translated  1008  from the base frame to the map frame  1010  supported by the mapping module. The bale poses are stored in the mapping module using the map frame of reference. As was discussed previously, the GPS coordinates of the bale can be optionally calculated  1014  and stored in the mapping module. The location of objects detected by the proximity sensor  1008  can be stored using the base frame of reference  1006  (or optionally in the map frame of reference). 
     The mapping module, also referred to herein as a world model, stores all bale poses in the map frame of reference. The world model is a container for all of the information about the local environment, such as the ground, objects, navigation information, bale pose, timestamps for the bale poses, etc. The world model operates independently of the stereo camera system and can handle data at different frame rates from different sources. As the autonomous bale mover moves around a field, for example, bale poses are updated automatically and continuously by the world model. As such, the bale poses are refined over time as new camera data is received (e.g., from different angles). Inputs (e.g., cameras, laser scanners) are processed continuously and fed into the world-model, which continuously aggregates all of the received information and provides increasingly accurate information about the location of bales to the rest of the system. All bale detections fed into the world model are accompanied by a confidence value, and detections with higher confidences “out-weigh” detections with lower confidences. 
     According to some embodiments, the world model operates in two dimensions (X and Z) rather than three dimensions. The world model assigns an error factor (e.g., a confidence factor) to a bale pose based on camera covariance in the X-Z positions, which results in an ellipse of uncertainty. This ellipse of uncertainty shrinks as more pose measurements are received for a particular bale. The world model can test the validity of received bale poses against the ellipse and reject poses that are outside of the ellipse (e.g., &gt;2 standard deviations). A received bale pose that falls within the ellipse is used to refine the bale pose. 
       FIG.  11    illustrates a method for updating the mapping module (e.g., world model) in accordance with various embodiments. The method shown in  FIG.  11    involves receiving  1002  the pose of a detected bale by the mapping module  108 . Instead of simply incorporating the received bale pose into the map frame supported by the mapping module, a check is made to determine the validity of the received bale pose. For example, the variability of the received bale pose relative to previously stored mapping data for the bale can be determined  1104  (e.g., using the ellipse of uncertainty). If the variability exceeds a threshold  1106 , the received poses discarded  1108 . If the variability does not exceed the threshold  1106 , the pose of the bale is updated  1110  in the mapping module. The variability (e.g., uncertainty ellipse) of the bale poses is updated  1112  in the mapping module. 
     Autonomous Bale Mover 
       FIGS.  12 - 18    are different views of an autonomous bale mover  1200  in accordance with various embodiments.  FIG.  12    is a top isometric view of the bale mover  1200 .  FIG.  13    is a front view of the bale mover  1200 .  FIG.  14    is a left view of the bale mover  1200 .  FIG.  15    is a left-front isometric view of the bale mover  1200 .  FIG.  16    is a rear view of the bale mover  1200 .  FIG.  17    is a right view of the bale mover  1200 .  FIG.  18    is a right-front isometric view of the baler  1200 . 
     The bale mover  1200  includes a bale loading system  1202 , a bale carrying system  1230 , and a ground-drive section  1260 . The bale loading system  1200  is illustrated coming into contact with a bale  122 . The bale loading system  1200  is configured to lift bales from the ground and to place them onto the bale carrying system  1230 . The bale carrying system  1230  comprises a conveyor system that defines a longitudinal carrying axis. The conveyor system is configured to hold a plurality of bales aligned end-to-end (face-to-face). As was discussed previously, bale pose estimation and mapping is executed on a continuous basis to identify the face  122 - f  of the bale  122  and to align the bale mover  1200  (e.g., the bale loading system  1200 ) with respect to the face  122 - f  of the bale  122  as the bale mover  1200  approaches the bale  122 . 
     The bale loading system  1202  includes a conveyor system comprising a left bale loading arm  1204  and a right bale loading arm  1210 . A brace  1208  is disposed between, and maintains positional stability of, the left and right bale loading arms  1204  and  1210 . The right bale loading arm  1210  supports a right conveyor track  1212 , which is configured to move relative to the periphery of the right bale loading arm  1210 . The right track  1212  is driven by a right motor  1211 , such as a hydraulic motor. The left bale loading arm  1204  supports a left conveyor track  1206 , which is configured to move relative to the periphery of the left bale loading arm  1204 . The left track  1206  is driven by a left motor  1205 , such as a hydraulic motor. Extending from the left bale loading arm  1204  is a sensor support  1220 . When loading bales, the left and right tracks  1206  and  1207  move in a rearward (R) direction. When unloading bales, the left and right tracks  1206  and  1207  move in a forward (F) direction. The sensor support  1220  is connected to and provides support for a sensor  102 . The sensor  102 , as discussed previously, includes a stereo camera system comprising a left camera  102   a  and a right camera  102   b.    
     The bale loading system  1202  is connected to the bale carrying system  1230  by left and right coupling arms  1214  and  1215 . A bed tilt cylinder  1209  is coupled to the front frame of the bale carrying system  1230  and to the left coupling arm  1214 . Actuation of the bed tilt cylinder  1209  raises and lowers the bale loading system  1202  relative to the ground. During transport of the autonomous bale mover  1200 , for example, the bale loading system  1202  is typically in the raised state. As the bale loading arms  1204  and  1210  advanced to, and make contact with, a bale  122 , the bale loading system  1202  transitions from the raised state to the lowered state. After picking up a bale  122 , the bale loading system  1202  moves to the raised state, and the bale mover  1200  searches for a new bale to pick up (or transports the bales to a bale stacking location). When in the raised state, the bale loading system  1202  conveys the picked-up bale rearwardly towards the bale carrying system  1230 . 
     The bale carrying system  1230  includes a conveyor system comprising a left conveyor arm  1232  and a right conveyor arm  1240 . The bale carrying system  1230  is designed to hold a predetermined number of bales aligned end-to-end (e.g., 3, 4, 5, 6, 7, 8 or 9 bales) received from the bale loading system  1202 . A brace  1284  is connected to, and positionally stabilizes, the left and right conveyor arms  1232  and  1240 . The left conveyor arm  1232  supports a left track  1234 , which is configured to move relative to the periphery of the left conveyor arm  1232 . The left track  1234  is driven by a left motor  1236 , such as a hydraulic motor. The right conveyor arm  1240  supports a right track  1244 , which is configured to move relative to the periphery of the right conveyor arm  1240 . The right track  1244  is driven by a right motor  1246 , such as a hydraulic motor. When loading bales, the left and right tracks  1234  and  1244  move in a rearward (R) direction. When unloading bales, the left and right tracks  1234  and  1244  move in a forward (F) direction. 
     The ground-drive section  1260  includes a power system, which includes an engine section  1262 , a hydraulics system  1263 , and an electronics section  1264 . The engine section  1262  houses an engine fluidically connected to a fuel tank  1263  and a cooling system. The hydraulic system  1263  includes hydraulic pumps connected to the various hydraulic motors of the bale mover  1200 . Details of the hydraulic system  1263  are shown in  FIGS.  23  and  24   . The ground-drive section  1260  is laterally offset from the bale carrying system  1230 , as is discussed hereinbelow. The ground-drive section  1260  includes a left track drive  1265  and a right track drive  1267 . The left track drive  1265  includes a left track  1266  movable via a left motor (see, e.g., left hydraulic motor  2314  shown in  FIG.  23   ), and the right track drive  1267  includes a right track  1268  movable via a right motor (see, e.g., right hydraulic motor  2312  shown in  FIG.  23   ). The ground-drive section  1260  is controlled by the electronics section  1264  to propel the autonomous bale mover  1200  along the ground via the left and right track drives  1265  and  1267 . 
     According to various embodiments, the autonomous bale mover  1200  includes a first proximity sensor  1270  and a second proximity sensor  1272 , the combination of which provides about 360° of proximity detection around the bale mover  1200  (see, e.g., proximity sensors  114   a  and  114   b  in  FIG.  3    and accompanying text). According to some embodiments, the first and second proximity sensors  1270  and  1272  are LIDAR sensors, such as laser scanners (e.g., 2-D laser scanners). A representative laser scanner is model LMS1XX (e.g., LMS111-10100) manufactured by SICK Vertriebs-GmbH. 
     Referring to  FIGS.  12  and  13   , it can be seen that the center of the ground-drive section  1260  is offset laterally from the center of the bale carrying system  1230  and that of the bale loading system  1202 . More particularly, the center of separation between the left and right track drives  1265  and  1267  is offset from the center of separation between the left and right conveyor arms  1232  and  1240  (and the left and right bale loading arms  1204  and  1210 ). As can be seen in  FIGS.  12  and  13   , the left track drive  1265  is positioned adjacent and below the power system of the bale mover (engine  1262 , hydraulic system  1263 , cooling system). The right track drive  1267  is positioned adjacent the longitudinal carrying axis of the bale carrying system  1230 . As shown, the right track drive  1267  is positioned between the left and right conveyor arms  1232  and  1240 , and biased toward the right conveyor arm  1240 . 
     It was determined after experimentation, that the location of the track drives  1265  and  1267  as shown in  FIGS.  12  and  13    allows the center of gravity to always be approximately over the drive tracks  1265  and  1267  irrespective of the load. It is noted that earlier-developed drive track arrangements suffered from not having enough weight on the drive tracks  1265  and  1267  for sufficient traction between loaded and unloaded states. As a result of trial and error, it was found that the “side delivery” configuration (engine section located to the side of the bale loading and conveying sections) provides a consistent center of gravity over the drive tracks  1265  and  1267  irrespective of the load on the bale loading system  1202  and/or bale carrying system  1230 . This “side delivery” configuration also uses the engine L to R to counterbalance when the bale mover is loaded with bales with a relatively higher center of gravity. 
     Bale Mover Control System 
       FIG.  19    is a block diagram of a control system for controlling an autonomous bale mover in accordance with various embodiments. The control system shown in  FIG.  19    can be implemented to control the autonomous bale mover shown in  FIGS.  12 - 18   , for example. The control system shown in  FIG.  19    includes a main computer  1246 , a microcontroller-based machine control unit  1930 , and various hardware  1950 . The main computer  1246 , which may be a Linux computer, is coupled to a number of sensors that are mounted on the bale mover. These sensors include the stereo camera system sensor  102  (comprising a left camera  102   a  and a right camera  102   b ), a GPS  1904 , and a pair of laser scanners  1270  and  1272 . The cameras  102   a  and  102   b  are coupled to a perception module  1906 . The perception module  1906  implements the various algorithms that are involved in bale detection, bale classification, and bale pose estimation. The output of the perception module  1906  is bale detections, specific to individual camera images, in the camera frame of reference. 
     A localization unit  1910  receives inputs from the GPS  1904 . The localization unit  1910  is responsible for calculating the best-guess of the bale mover&#39;s pose (location and heading) in the map frame of reference. Inputs to the localization unit  1910  include absolute heading (e.g., 3° North of East), absolute position (e.g., 5 m north and 2 m east of the map frame origin), relative heading (also known as rotational velocity, e.g., 3° clockwise since last reading), and relative position (also known as forward velocity, 0.5 m forward of last position). The output of the localization unit  1910  is pose of the machine (used as origin of the base frame of reference), as the absolute position and the absolute heading within the map frame. The laser scanners  1270  and  1272  can be input to the world model  1908 , such that the locations of objects in the vicinity of the bale mover are identified with respect to the map frame of reference. According to some embodiments, when the bale mover is within a specified distance (e.g., ˜10 m) of a bale, output from the sensor  102  and laser scanners  1270  and  1272  can be directed to the localization unit  1920  so that detections are identified with respect to the base frame of reference, which alleviates issues of GPS drift and various localization inaccuracies, for example. 
     A world model  1908  stores bale locations in the map frame of reference. The world model, also referred to herein as a mapping module, takes individual detections from the perception unit  1906 , transforms these detections into the map frame of reference with information from the TF unit  1912 , and uses statistical models to determine the likely location of bales in the map frame of reference. The world model  1908  is responsible for setting the threshold of certainty (e.g., the ellipse of uncertainty) to pursue picking up the bale, and updating the locations of those bails as more information is gathered. 
     The TF unit  1912  is a utility of the ROS (Robot Operating System) which keeps track of the relationships between the various different frames of reference with respect to time. As discussed previously, embodiments of an autonomous bale mover use a multiplicity of reference frames, including a camera frame, a base frame, and a map frame. The TF unit  1912  takes in information about one frame of reference with respect to time and transforms this information relative to another frame of reference with respect to time. For example, the TF unit  1912  takes in information like “at time X, the origin of frame ‘base’ is at a certain Cartesian coordinate (5,28) relative to the origin of frame ‘map’, and at a 90° angle clockwise.” The TF module  1912  outputs the same data, but at a requested time. The TF unit  1912  also handles transitive properties of projection (e.g., if frame X is at a Pose X within frame Y, and frame Y is at Pose Y within frame Z, where is frame X with relation to frame Z?). The TF unit  1912  also contains the static (never moves) location of the camera frame relative to the base frame. 
     The primary autonomy state machine  1914  makes decisions based on the state of both the bale mover and the world model  1908  (see, e.g., the flow chart shown in  FIG.  20   ). Outputs from the primary autonomy state machine  1914  include desired velocity, rotational velocity, and commands to raise, lower, start, and stop the bale pickup tracks (e.g., left and right tracks  1206  and  1212  shown in  FIG.  12   ). The primary autonomy state machine  1914  is shown coupled to the world model  1908  and a CAN transmitter (TX)  1916 . The main computer  1246  also includes a CAN receiver (RX)  1918 . The CAN TX  1916  and CAN RX  1918  are part of a Controller Area Network (CAN bus) of the bale mover. The CAN TX  1916 /RX  1918  is the software unit which communicates on the CAN bus with the microcontroller-based machine control unit  1932 . A representative CAN TX  1916 /RX  1918  unit is a Universal Transceiver Model No. B424-A, available from Vermeer Corporation, Pella Iowa. 
     The CAN TX  1916  and CAN RX  1918  of the main computer  1902  are coupled to a CAN controller  1934  of the microcontroller-based machine control unit  1932 . A representative machine control unit  1932  is Controller Model No. C248, available from Vermeer Corporation. The CAN controller  1934  mirrors the utility of the CAN TX  1916  and CAN RX  1918 . The CAN controller  1934  receives commands (e.g., desired velocity, desired rotational velocity) from the CAN TX  1916  and issues messages to a hardware state machine  1936 . The CAN controller  1934  produces various outputs (e.g., left wheel speed, right wheel speed, command Ack/Nack) which are received by the CAN RX  1918 . Using the outputs received from the CAN controller  1934 , the CAN RX  1918  communicates velocity and rotational velocity to the localization unit  1910  of the main computer  1246 . The machine control unit  1930  produces a number of outputs that are received by various hardware components  1950  of the bale mover. The machine control unit  1930  also receives input information from the hardware components  1950 . 
     The hardware state machine  1936  of the machine control unit  1930  controls the state of discrete outputs  1940 ,  1942 ,  1944 , which control different hydraulic valves of the bale mover. For example, the discrete outputs  1940  and  1942  control discrete hydraulic valves  1956  and  1958  which cooperate to control the movement of a bed tilt cylinder  1209  of the bale mover. The bed tilt cylinder  1209  raises and lowers the bale loading system  1202  shown in  FIG.  12   , for example. A discrete output  1944  cooperates with a discrete hydraulic valve  1962  to control the bale track engage circuit  1968 . The bale track engage circuit  1968  controls actuation of the left and right tracks  1206  and  1212  of the bale loading system  1202  shown in  FIG.  12   , for example. 
     The machine control unit  1930  includes a PID (proportional-integral-derivative) controller or loop  1914 . The PID controller  1914  continuously calculates an error value as the difference between a desired setpoint and a measured process variable. The PID controller  1938  receives velocity and rotational velocity from the CAN controller  1934 , and produces outputs to a first H-bridge output  1946  and a second H-bridge output  1948 . The output of the first H-bridge output  1946  is communicated to a proportional hydraulic pump control  1952 , which controls the left track motor  1964  of the left track drive  1265  shown in  FIG.  12   . The output of the second H-bridge output  1948  is communicated to a proportional hydraulic pump control  1954 , which controls the right track motor  1966  of the right track drive  1267  shown in  FIG.  12   . The bale mover also includes wheel encoders  1962  which are coupled to a pulse frequency input  1949  of the machine control unit  1930 . An output from the pulse frequency input  1949  is coupled to the PID controller  1914 . 
     Hydraulic System 
       FIG.  23    illustrates a hydraulic system for an autonomous bale mover in accordance with various embodiments. The hydraulic system shown in  FIG.  23    can be implemented on the autonomous bale mover illustrated in  FIGS.  12 - 18   , for example. As was discussed previously, the hydraulic system  1263  includes various hydraulic pumps, hydraulic motors, and hydraulic control valves. As is shown in  FIG.  23   , the hydraulic system  1263  includes a right hydraulic pump  2320  and a left hydraulic pump  2322 . The right hydraulic pump  2320  is fluidically coupled to a front conveyor control valve  2304 , and the left hydraulic pump  2322  is fluidically coupled to a rear conveyor control valve  2302 . The front conveyor control valve  2304  is fluidically coupled to the left and right hydraulic motors  1205  and  1211  of the bale loading system  1202  (e.g., the front conveyor). The left and right hydraulic motors  1205  and  1211  of the bale loading system  1202  can be controlled to move the front left and right conveyor tracks  1206  and  1212  in forward and reverse directions. The rear conveyor control valve  2302  is fluidically coupled to the left and right hydraulic motors  1236  and  1246  of the bale carrying system  1230  (e.g., the rear conveyor). The left and right hydraulic motors  1236  and  1246  of the bale carrying system  1230  can be controlled to move the rear left and right conveyor tracks  1234  and  1244  in forward and reverse directions. 
     The right hydraulic pump  2320  is also fluidically coupled to a right hydraulic ground-drive motor  2312 , which controls forward and reverse movement of the right ground-drive track  1268 . The left hydraulic pump  2322  is fluidically coupled to a left hydraulic ground-drive motor  2314 , which controls forward and reverse movement of the left ground-drive track  1266 . 
     According to various embodiments, the engine  1262  drives an H1 Series tandem hydrostatic pump (right and left hydraulic pumps  2320  and  2322 ) manufactured by Danfoss. Each of the Danfoss hydraulic pumps  2320  and  2322  has 53.8 cc of displacement. The right and left ground-drive motors  2312  and  2314  can be MSE05-22.9 ci displacement motors manufactured by Poclain. The rear and front conveyor control valves  2302  and  2304  can be Hydraforce 4-port selector valves (three-position, closed center). These control valves  2302  and  2304  can pass oil straight through or divert the oil through the corresponding conveyor motors in series. The third position of the control valves  2302  and  2304  allows reversing of the conveyor motors which can be incorporated in some embodiments. 
     As can be seen in  FIG.  23   , the left hydraulic drive circuit and the rear conveyor tracks  1234  and  1244  are combined, and the right hydraulic drive circuit and the front conveyor tracks  1206  and  1212  are combined, but this is not a requirement. When normally driving through the field, the control valves  2302  and  2304  are in the neutral position, such that no oil passes through the conveyor motors  1205 ,  1211 ,  1236 , and  1246 . During bale pickup, the control valves  2302  and  2304  shift to pass oil in series to the conveyor motors  1205 ,  1211 ,  1236 , and  1246  to turn them as well as the ground drive motors  2312  and  2314 . 
     Operating Scenario—Example #1 
       FIG.  20    is a flow chart illustrating various operations of an autonomous bale mover in accordance with various embodiments. In  FIG.  20   , it is assumed that the world model  1908  provides known bale location data, and that the bale mover is operating within a predefined field. A check is made  2002  to determine if known bale locations are available from the world model  1908 . If not, the autonomous bale mover traverses  2004  as-yet unseen parts of the field. If known bale locations are available, there may be a number of bale locations that are available. According to various embodiments, the bail location nearest the bale mover is selected  2006 . 
     The bale mover drives  2008  to a location in line with the bale until it reaches a location approximately 8 yards away from the bale. While the bale mover continues to move towards the bale at this location (at the same speed or perhaps slowing down somewhat), the bale mover also lowers and activates the bale pick up tracks  1206  and  1212  of the bale loading system  1202  shown in  FIG.  12   . The bale mover continues to move toward the bale and engage the bale while moving forward (not stopping) with the bale arm tracks  1206  and  1212  running, thereby picking up the bale on the move. 
     After picking up the bale, the bale loading system  1202  is raised  2014  and the bale arm tracks  1206  and  1212  are stopped. A check is made  2016  to determine if the bale mover is at full bale capacity (e.g., n bales, where n is an integer between 2 and 9). For example, according to some embodiments, the bale mover is configured to load and carry three bales. If the bale mover is not currently at full capacity, a check is made  2002  on the availability of the next known bale location. If the bale mover is currently at full capacity, the bale mover drives  2018  to a predetermined stacking location. The bale mover drives in alignment with previously unloaded bales in the stacking area until a location about 8 yards away from the last bale is reached. At this point, the bale loading system  1202  is lowered and the bale mover drives forward until the bale mover touches the last stacked bale. 
     At this point, the left and right tracks  1206  and  1212  of the bale loading system  1202  and the left and right tracks  1234  and  1244  of the bale carrying system  1230  are activated. The bale mover then drives in a reverse direction for about 15 yards while the bales are unloaded from the bale mover. After reaching the 15 yard location, the tracks  1206  and  1212  of the bale loading system  1202  and the tracks  1234  and  1244  of the bale carrying system  1230  are deactivated, while the bale mover continues to back away from the bale stack. Processing continues with checking  2002  the availability of a known bale location. 
     Operating Scenario—Example #2 
       FIG.  21    illustrates an autonomous bale mover  1200  moving through a field  2100  and picking up bales  2104  in accordance with various embodiments. The field  2100  is typically identified by a geo-fence in the world model within which the bale mover  1200  is free to operate. In  FIG.  21   , a number of bales  2004  are distributed along a route  2102  previously taken by a baler machine when creating the bales  2004 . The objective of the bale mover  1200  is to pick up the bales  2104  in a time efficient manner, without stopping to pick up individual bales  2104 . Given the distribution of the bales  2104  in  FIG.  21   , it would appear that the most efficient path through the field  2100  is in a direction generally orthogonal to the baler machine route  2102 . The actual route taken by the bale mover  1200  can be based on the methodology illustrated in  FIG.  20   , for example, which uses a next-nearest bale approach for prioritizing the bales  2104  for pickup. 
     According to some embodiments, the bale mover  1200  can move through the field  2100  at an average speed of about 5 mph, with little or no slowdown when approaching and picking up bales  2104 . In some embodiments, the bale mover  1200  can move at an average speed of about 4 to 8 MPH when contacting and picking up bales  2104 , and move at a higher average speed of between 5 and 15 MPH when traversing relatively long distances between individual bales  2104  or to/from a predetermined stacking location  2106 . 
     It is noted that an autonomous bale mover according to the disclosure does not require a consistent pattern of setting the bales in the field. For example, bales can be distributed randomly in the field or in a consistent pattern, with little difference in efficiency when picking up the bales in either scenario. 
     Operating Scenario—Example #3 
       FIG.  22    is a three-dimensional graph showing the time to collect bales versus machine capacity and travel speed in accordance with various embodiments. In the graph shown in  FIG.  22   , the x-axis is bale capacity (maximum number of bales that can be carried by the bale mover), the y-axis is bale mover travel speed (mph), and the z-axis is elapsed time (hours). The graph shown in  FIG.  22    is a summary of estimations of how long it would take to pick up bales off of the field for different bale capacities and bale mover speeds. It is to be understood that there are many variables, such as field size, stack location, field bale density, etc., that impact the estimations. 
     In  FIG.  22   , it is assumed that the field is 160 acres in area, there are 2.5 bales per acre with 30 foot rows. It was speculated that between about 30 and 40 hours would be the maximum time that users would deem reasonable to pick up bales in a 160 acre field in order to keep up with the baler machines that produced the bales. From the graph shown in  FIG.  22   , it can be seen that at an average of 5 mph, with a bale capacity of three, an autonomous bale mover could clear a typical 160 acre field in about 29 hours. 
     This document discloses numerous embodiments, including but not limited to the following: 
     Item 1 is a method, comprising: 
     scanning a region of land using a sensor comprising stereo cameras; 
     producing, by the sensor, an image and disparity data for the image; 
     searching for a vertical object within the image using the disparity data; 
     determining whether the vertical object is a bale of material using the image; and 
     computing an orientation of the bale relative to the sensor using the disparity data. 
     Item 2 is the method of item 1, further comprising: 
     producing modified disparity data by removing disparity data corresponding to the ground in the image; and 
     searching for the vertical object within the region using the modified disparity data. 
     Item 3 is the method of item 1, wherein searching for the vertical object comprises searching for a vertical object having a height relative to the ground in the image that is greater than a threshold. 
     Item 4 is the method of item 1, wherein searching for the vertical object comprises scanning the image using a detection window having a predetermined size in terms of pixels. 
     Item 5 is the method of item 4, wherein the predetermined size of the detection window corresponds to a size of the bale at a given distance separating the vertical object from the sensor. 
     Item 6 is the method of item 1, wherein determining whether the vertical object is a bale comprises: 
     extracting features of the vertical object; and 
     classifying the vertical object using the extracted features. 
     Item 7 is the method of item 6, wherein classifying the vertical object comprises: 
     classifying the vertical object using a plurality of classifiers; and 
     determining that the vertical object is the bale in response to each of the plurality of classifiers successfully classifying the vertical object as the bale. 
     Item 8 is the method of item 1, wherein determining whether the vertical object is the bale comprises: 
     classifying, by a first classifier, the vertical object using first features of the object; and 
     if the first classifier indicates the vertical object is likely the bale, classifying, by a second classifier, the vertical object using second features of the object. 
     Item 9 is the method of item 8, wherein: 
     the first classifier comprises a first support vector machine; 
     the first features are Haar features; 
     the second classifier comprises a second support vector machine; and 
     the second features are HOG (Histogram of Oriented Gradients) features. 
     Item 10 is the method of item 1, wherein determining bale orientation comprises computing position of the bale relative to the sensor using the disparity data. 
     Item 11 is the method of item 1, wherein determining bale orientation comprises: 
     computing three-dimensional points (X, Y, Z) for the bale within the image using the disparity data; 
     projecting X and Z coordinates of the three-dimensional points to a two-dimensional (X-Z) plane corresponding to a top-down view of the bale; 
     determining a face of the bale and a side of the bale using the two-dimensional plane; and 
     computing an orientation of the bale relative to the sensor using the face of the bale. 
     Item 12 is the method of item 11, wherein determining the face and side of the bale comprises: 
     generating a first best fit line through points in the X-plane; 
     generating a second best fit line through points in the Z-plane; and 
     determining which of the first and second best fit lines represents the face of the bale. 
     Item 13 is the method of item 12, wherein: 
     determining which of the first and second best fit lines represents the face of the bale comprises determining disparity data variation for the first and second best fit lines; and 
     the best fit line with the smallest variation corresponds to the face of the bale. 
     Item 14 is the method of item 1, further comprising: 
     storing orientation and a position of the bale by a world model; and 
     updating the orientation and position of the bale in the world model in response to subsequent imaging of the bale by the sensor. 
     Item 15 is the method of item 1, further comprising: 
     receiving current orientation and current position of the bale by a world model; 
     determining variability of the current orientation and current position relative to orientation and position data previously stored in the world model for the bale; and 
     updating the orientation and position of the bale in the world model to include the current orientation and current position if the variability does not exceed a threshold. 
     Item 16 is the method of item 15, further comprising discarding the current orientation and current position if the variability exceeds the threshold. 
     Item 17 is an apparatus, comprising: 
     a sensor comprising a left camera and a right camera; and 
     a processor coupled to the sensor and configured to:
         produce an image and disparity data for the image;   search for a vertical object within the image using the disparity data;   determine whether the vertical object is a bale of material using the image; and   compute an orientation of the bale relative to the sensor using the disparity data.
 
Item 18 is the apparatus of item 17, wherein the processor is configured to:
       

     produce modified disparity data by removing disparity data corresponding to the ground in the image; and 
     search for the vertical object within the region using the modified disparity data. 
     Item 19 is the apparatus of item 17, wherein the processor is configured to search for a vertical object having a height relative to the ground in the image that is greater than a threshold. 
     Item 20 is the apparatus of item 17, wherein the processor is configured to search for the vertical object by scanning the image using a detection window having a predetermined size in terms of pixels. 
     Item 21 is the apparatus of item 20, wherein the predetermined size of the detection window corresponds to a size of the bale at a given distance separating the vertical object from the sensor. 
     Item 22 is the apparatus of item 17, wherein the processor is configured to determine whether the vertical object is a bale by: 
     extracting features of the vertical object; and 
     classifying the vertical object using the extracted features. 
     Item 23 is the apparatus of item 22, wherein the processor is configured to classify the vertical object by: 
     classifying the vertical object using a plurality of classifiers; and 
     determining that the vertical object is the bale in response to each of the plurality of classifiers successfully classifying the vertical object as the bale. 
     Item 24 is the apparatus of item 23, wherein the processor is configured to determine whether the vertical object is the bale by: 
     classifying, by a first classifier, the vertical object using first features of the object; and 
     if the first classifier indicates the vertical object is likely the bale, classifying, by a second classifier, the vertical object using second features of the object. 
     Item 25 is the apparatus of item 24, wherein: 
     the first classifier comprises a first support vector machine; 
     the first features are Haar features; 
     the second classifier comprises a second support vector machine; and 
     the second features are HOG (Histogram of Oriented Gradients) features. 
     Item 26 is the apparatus of item 17, wherein the processor is configured to determine bale orientation by computing position of the bale relative to the sensor using the disparity data. 
     Item 27 is the apparatus of item 17, wherein the processor is configured to determine bale orientation by: 
     computing three-dimensional points (X, Y, Z) for the bale within the image using the disparity data; 
     projecting X and Z coordinates of the three-dimensional points to a two-dimensional (X-Z) plane corresponding to a top-down view of the bale; 
     determining a face of the bale and a side of the bale using the two-dimensional plane; and 
     computing an orientation of the bale relative to the sensor using the face of the bale. 
     Item 28 is the apparatus of item 27, wherein the processor is configured to determine the face and side of the bale by: 
     generating a first best fit line through points in the X-plane; 
     generating a second best fit line through points in the Z-plane; and 
     determining which of the first and second best fit lines represents the face of the bale. 
     Item 29 is the apparatus of item 28, wherein the processor is configured to: 
     determine which of the first and second best fit lines represents the face of the bale by determining disparity data variation for the first and second best fit lines; and 
     the best fit line with the smallest variation corresponds to the face of the bale. 
     Item 30 is the apparatus of item 17, wherein the processor is configured to: 
     store orientation and a position of the bale by a world model; and 
     update the orientation and position of the bale in the world model in response to subsequent imaging of the bale by the sensor. 
     Item 31 is the apparatus of item 17, wherein the processor is configured to: 
     receive current orientation and current position of the bale by a world model; 
     determine variability of the current orientation and current position relative to orientation and position data previously stored in the world model for the bale; and 
     update the orientation and position of the bale in the world model to include the current orientation and current position if the variability does not exceed a threshold. 
     Item 32 is the apparatus of item 31, wherein the processor is configured to discard the current orientation and current position if the variability exceeds the threshold. 
     Item 33 is the apparatus of item 17, further comprising a bale mover, wherein the sensor and computer are components of the bale mover. 
     Item 34 is the apparatus of item 17, further comprising an autonomous bale mover, wherein the sensor and computer are components of the bale mover. 
     Item 35 is an autonomous bale mover, comprising: 
     an integral power system; 
     a ground-drive system; 
     a bale loading system; 
     a bale carrying system; 
     a control system capable of providing control signals for the ground-drive system to control the speed of and direction of travel of the bale mover and to control operation of the bale loading system and the bale carrying system; and 
     a sensor system for detecting the position and orientation of bales. 
     Item 36 is the autonomous bale mover of item 35, wherein: 
     the bale carrying system comprises a conveyor system that defines a longitudinal carrying axis, the conveyor system configured to hold a plurality of bales aligned end-to-end; 
     the bale loading system is configured to lift bales from the ground and to place them onto the bale carrying system; 
     the integral power system comprises an engine, a cooling system, and a hydraulic system and is positioned adjacent and laterally offset from the bale carrying system; and 
     the ground-drive system comprises a pair of tracks, a first track positioned adjacent the longitudinal carrying axis and a second track positioned adjacent the integral power system. 
     Item 37 is the autonomous bale mover of item 36, wherein: 
     the first track of the ground-drive system is positioned underneath the integral power system; and 
     the second track of the ground-drive system is positioned between left and right conveyor arms of the bale carrying system. 
     Item 38 is the autonomous bale mover of item 37, wherein the second track of the ground-drive system is biased toward the right conveyor arm of the bale carrying system. 
     Item 39 is the autonomous bale mover of item 35, wherein: 
     the bale loading system comprises a left conveyor track and a right conveyor track; and 
     the bale carrying system comprises a left conveyor track and a right conveyor track. 
     Item 40 is the autonomous bale mover of item 39, wherein: 
     the left and the right conveyor tracks of the bale loading system and the bale carrying system move in a reverse direction when loading bales onto the bale mover; and 
     the left and the right conveyor tracks of the bale loading system and the bale carrying system move in a forward direction when unloading bales from the bale mover. 
     Item 41 is the autonomous bale mover of item 35, wherein the control system comprises: 
     a main computer coupled to the sensors; and 
     a microcontroller-based machine control unit coupled to the main computer. 
     Item 42 is the autonomous bale mover of item 41, wherein: 
     the main computer comprises a CAN transmitter and a CAN receiver; and 
     the machine control unit comprises a CAN controller. 
     Item 43 is the autonomous bale mover of item 42, wherein the machine control unit comprises a PID controller and a hardware state machine respectively coupled to the CAN controller. 
     Item 44 is the autonomous bale mover of item 41, wherein the machine control unit comprises a plurality of outputs each coupled to a hydraulic pump control or a hydraulic valve of the bale mover. 
     Item 45 is the autonomous bale mover of item 44, wherein: 
     a first output is coupled to a first hydraulic pump control that controls a left track motor of the bale mover; 
     a second output is coupled to a second hydraulic pump control that controls a right track motor of the bale mover; 
     a third output and a fourth output are coupled to respective first and second hydraulic valves that are coupled to a bed tilt cylinder that controls raising and lowering of the bale loading system; and 
     a fifth output coupled to a third hydraulic valve which is coupled to a bale track engage circuit for operating conveyor tracks of the bale loading system and the bale carrying system. 
     Item 46 is the autonomous bale mover of item 35, wherein the sensor system comprises a stereo camera system. 
     Item 47 is the autonomous bale mover of item 46, wherein the sensor system comprises a processor coupled to the stereo camera system, the processor configured to: produce an image and disparity data for the image; 
     search for a vertical object within the image using the disparity data; 
     determine whether the vertical object is a bale of material using the image; and 
     compute the position and orientation of the bale relative to the bale mover using the disparity data. 
     Item 48 is the apparatus of item 47, wherein the processor is configured to: 
     produce modified disparity data by removing disparity data corresponding to the ground in the image; and 
     search for the vertical object within the region using the modified disparity data. 
     Item 49 is the apparatus of item 47, wherein the processor is configured to search for a vertical object having a height relative to the ground in the image that is greater than a threshold. 
     Item 50 is the apparatus of item 47, wherein the processor is configured to search for the vertical object by scanning the image using a detection window having a predetermined size in terms of pixels. 
     Item 51 is the apparatus of item 50, wherein the predetermined size of the detection window corresponds to a size of the bale at a given distance separating the vertical object from the sensor. 
     Item 52 is the apparatus of item 47, wherein the processor is configured to determine whether the vertical object is a bale by: 
     extracting features of the vertical object; and 
     classifying the vertical object using the extracted features. 
     Item 53 is the apparatus of item 52, wherein the processor is configured to classify the vertical object by: 
     classifying the vertical object using a plurality of classifiers; and 
     determining that the vertical object is the bale in response to each of the plurality of classifiers successfully classifying the vertical object as the bale. 
     Item 54 is the apparatus of item 53, wherein the processor is configured to determine whether the vertical object is the bale by: 
     classifying, by a first classifier, the vertical object using first features of the object; and 
     if the first classifier indicates the vertical object is likely the bale, classifying, by a second classifier, the vertical object using second features of the object. 
     Item 55 is the apparatus of item 54, wherein: 
     the first classifier comprises a first support vector machine; 
     the first features are Haar features; 
     the second classifier comprises a second support vector machine; and 
     the second features are HOG (Histogram of Oriented Gradients) features. 
     Item 56 is the apparatus of item 47, wherein the processor is configured to determine bale orientation by computing position of the bale relative to the sensor using the disparity data. 
     Item 57 is the apparatus of item 47, wherein the processor is configured to determine bale orientation by: 
     computing three-dimensional points (X, Y, Z) for the bale within the image using the disparity data; 
     projecting X and Z coordinates of the three-dimensional points to a two-dimensional (X-Z) plane corresponding to a top-down view of the bale; 
     determining a face of the bale and a side of the bale using the two-dimensional plane; and 
     computing an orientation of the bale relative to the sensor using the face of the bale. 
     Item 58 is the apparatus of item 57, wherein the processor is configured to determine the face and side of the bale by: 
     generating a first best fit line through points in the X-plane; 
     generating a second best fit line through points in the Z-plane; and 
     determining which of the first and second best fit lines represents the face of the bale. 
     Item 59 is the apparatus of item 58, wherein the processor is configured to: 
     determine which of the first and second best fit lines represents the face of the bale by determining disparity data variation for the first and second best fit lines; and 
     the best fit line with the smallest variation corresponds to the face of the bale. 
     Item 60 is the apparatus of item 47, wherein the processor is configured to: 
     store orientation and a position of the bale by a world model; and 
     update the orientation and position of the bale in the world model in response to subsequent imaging of the bale by the sensor. 
     Item 61 is the apparatus of item 47, wherein the processor is configured to: 
     receive current orientation and current position of the bale by a world model; 
     determine variability of the current orientation and current position relative to orientation and position data previously stored in the world model for the bale; and 
     update the orientation and position of the bale in the world model to include the current orientation and current position if the variability does not exceed a threshold. 
     Item 62 is the apparatus of item 61, wherein the processor is configured to discard the current orientation and current position if the variability exceeds the threshold. 
     Item 63 is the apparatus of item 35, further comprising proximity sensor for sensing objects in proximity of the bale mover. 
     Item 64 is the apparatus of item 63, wherein the proximity sensors comprise laser scanners. 
     Item 65 is the apparatus of item 63, wherein the proximity sensors comprise ultrasonic sensors. 
     Item 66 is the apparatus of item 63, wherein: 
     at least a first proximity sensor is situated on one side of the bale mover; and 
     at least a second proximity sensor is situated on the other side of the bale mover. 
     Item 67 is the apparatus of item 63, wherein the proximity sensors provide about 360° of proximity sensing around the periphery of the bale mover. 
     Item 68 is the apparatus of item 63, wherein the control system is configured to adjust one or both of the speed and travel direction of the bale mover in response to signals produced by the proximity sensors. 
     Item 69 is the apparatus of item 68, wherein the control system is configured to reduce the speed of or stop the bale mover in response to signals produced by the proximity sensors to avoid contact with an object other than a bale. 
     Item 70 is a method implemented with use of an autonomous bale mover, comprising: 
     defining a region within which the autonomous bale mover operates; 
     locating bales distributed within the region by the bale mover as the bale mover moves through the region; 
     picking up located bales by the bale mover without stopping; 
     transporting picked-up bales to a predetermined stacking location within the region by the bale mover; and 
     continuing to locate and pick up bales within the region by the bale mover until all bales within the region are transported to the predetermined stacking location. 
     Item 71 is the method of item 70, wherein defining the region comprises defining a geo-fence around the region within which the autonomous bale mover operates. 
     Item 72 is the method of item 70, wherein the bale mover picks up a predetermined number of bales and then transports the predetermined number of bales to the predetermined stacking location. 
     Item 73 is the method of item 72, where the predetermined number of bales is a number between 2 and 9. 
     Item 74 is the method of item 72, where the predetermined number of bales is a number between 3 and 5. 
     Item 75 is the method of item 70, wherein picking up the bales is implemented using a next-nearest bale methodology. 
     Item 76 is the method of item 70, wherein the bale mover unloads the picked-up bales in rows at the predetermined stacking location. 
     Item 77 is the method of item 70, wherein locating the bales comprises distinguishing between a face of the bales and a side of the bales. 
     Item 78 is the method of item 70, wherein locating the bales comprises orienting the bale mover relative to a face of the bales when picking up the bales. 
     Item 79 is the method of item 70, wherein picking up the bales comprises: 
     picking up the bales using a bale loading system of the bale mover; and 
     conveying the picked-up bales from the bale loading system to a bale carrying system of the bale mover. 
     Item 80 is the method of item 79, wherein conveyor tracks of the bale loading system pick up the bales and convey the picked-up bales to the bale carrying system. 
     Item 81 is the method of item 80, wherein conveyor tracks of the bale carrying system move picked-up bales received from the bale loading system toward the back of the bale carrying system. 
     Item 82 is the method of item 70, wherein locating the bales comprises visually locating the bales using a stereo camera system. 
     Item 83 is the method of item 82, wherein visually locating the bales comprises detecting vertical objects in images produced by the stereo camera system and determining whether the vertical objects are bales. 
     Item 84 is the method of item 83, wherein detecting vertical objects in the images comprises scanning disparity maps of the images using a detection window having a size corresponding to a size of the bales at a given distance separating the vertical objects from the bale mover.
 
Item 85 is the method of item 83, wherein determining whether the vertical objects are bales comprises:
 
     extracting features of the vertical objects; and 
     classifying the vertical objects using the extracted features. 
     Item 86 is the method of item 85, wherein classifying the vertical object comprises: 
     classifying the vertical objects using a plurality of classifiers; and 
     determining that the vertical objects is the bale in response to each of the plurality of classifiers successfully classifying the vertical objects as bales. 
     Item 87 is the method of item 83, wherein determining whether the vertical object is the bale comprises: 
     classifying, by a first classifier, the vertical objects using first features of the objects; and 
     if the first classifier indicates the vertical objects are likely bales, classifying, by a second classifier, the vertical objects using second features of the objects. 
     Item 88 is the method of item 87, wherein: 
     the first classifier comprises a first support vector machine; 
     the first features are Haar features; 
     the second classifier comprises a second support vector machine; and 
     the second features are HOG (Histogram of Oriented Gradients) features. 
     Item 89 is the method of item 82, further comprising determining bale orientation by computing position of the bales relative to the stereo camera system using disparity data. 
     Item 90 is the method of item 89, wherein determining bale orientation comprises: 
     computing three-dimensional points (X, Y, Z) for the bales using the disparity data; 
     projecting X and Z coordinates of the three-dimensional points to a two-dimensional (X-Z) plane corresponding to a top-down view of the bales; 
     determining a face of the bales and a side of the bales using the two-dimensional plane; and 
     computing an orientation of the bales relative to the sensor using the face of the bales. 
     Item 91 is the method of item 90, wherein determining the face and side of the bale comprises: 
     generating a first best fit line through points in the X-plane; 
     generating a second best fit line through points in the Z-plane; and 
     determining which of the first and second best fit lines represents the face of the bales. 
     Item 92 is the method of item 91, wherein: 
     determining which of the first and second best fit lines represents the face of the bales comprises determining disparity data variation for the first and second best fit lines; and 
     the best fit line with the smallest variation corresponds to the face of the bales. 
     Item 93 is the method of item 82, further comprising: 
     storing an orientation and a position of the bales by a world model; and 
     updating the orientation and position of the bales in the world model in response to subsequent imaging of the bales by the stereo camera system. 
     Item 94 is the method of item 82, further comprising: 
     receiving current orientation and current position of the bales by a world model; 
     determining variability of the current orientation and current position relative to orientation and position data previously stored in the world model for the bales; and 
     updating the orientation and position of the bales in the world model to include the current orientation and current position if the variability does not exceed a threshold. 
     Item 95 is the method of item 94, further comprising discarding the current orientation and current position if the variability exceeds the threshold. 
     The discussion and illustrations provided herein are presented in an exemplary format, wherein selected embodiments are described and illustrated to present the various aspects of the present invention. Systems, devices, or methods according to the present invention may include one or more of the features, structures, methods, or combinations thereof described herein. For example, a device or system may be implemented to include one or more of the advantageous features and/or processes described below. A device or system according to the present invention may be implemented to include multiple features and/or aspects illustrated and/or discussed in separate examples and/or illustrations. It is intended that such a device or system need not include all of the features described herein, but may be implemented to include selected features that provide for useful structures, systems, and/or functionality. 
     Although only examples of certain functions may be described as being performed by circuitry for the sake of brevity, any of the functions, methods, and techniques can be performed using circuitry and methods described herein, as would be understood by one of ordinary skill in the art.