Patent Publication Number: US-11662739-B2

Title: Method, system and apparatus for adaptive ceiling-based localization

Description:
BACKGROUND 
     Environments in which objects are managed, such as retail facilities, warehousing and distribution facilities, and the like, may be complex and fluid. For example, a retail facility may include objects such as products for purchase, and a distribution facility may include objects such as parcels or pallets. The visual and structural features of such facilities may also vary widely. A mobile automation apparatus may be deployed within such facilities to perform tasks at various locations. For example, a mobile automation apparatus may be deployed to capture data relating to these objects at various locations in a retail, warehousing, or distribution facility. To navigate to the appropriate locations, the mobile automation apparatus may track its own location within the facility. The complexity and variability of the facility may reduce the accuracy of the apparatus&#39; localization. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed invention, and explain various principles and advantages of those embodiments. 
         FIG.  1    is a schematic of a mobile automation system. 
         FIG.  2    depicts a mobile automation apparatus in the system of  FIG.  1   . 
         FIG.  3    is a block diagram of certain internal components of the mobile automation apparatus in the system of  FIG.  1   . 
         FIG.  4    is a flowchart of a method of adaptive ceiling-based localization in the system of  FIG.  1   . 
         FIG.  5    is a diagram illustrating localization in a primary localization mode according to the method of  FIG.  4   . 
         FIG.  6    is a diagram illustrating conditions preventing primary localization according to the method of  FIG.  4   . 
         FIG.  7    is a diagram illustrating localization in a secondary localization mode according to the method of  FIG.  4   . 
         FIG.  8    is a diagram illustrating conditions preventing secondary localization according to the method of  FIG.  4   . 
     
    
    
     Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention. 
     The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. 
     DETAILED DESCRIPTION 
     Examples disclosed herein are directed to a method in a navigational controller including: controlling a ceiling-facing camera of a mobile automation apparatus to capture a stream of images of a facility ceiling; activating a primary localization mode including: (i) detecting primary features in the captured image stream; and (ii) updating, based on the primary features, an estimated pose of the mobile automation apparatus and a confidence level corresponding to the estimated pose; determining whether the confidence level exceeds a confidence threshold; when the confidence level does not exceed the threshold, switching to a secondary localization mode including: (i) detecting secondary features in the captured image stream; (ii) updating the estimated pose and the confidence level based on the secondary features; and (iii) searching the image stream for the primary features; and responsive to detecting the primary features in the image stream, re-activating the primary localization mode. 
     Additional examples disclosed herein are directed to a mobile automation apparatus comprising: a chassis; a ceiling-facing camera supported by the chassis; and 
     a navigational controller configured to: control the ceiling-facing camera to capture a stream of images of a facility ceiling; activate a primary localization mode to: 
     (i) detect primary features in the captured image stream; and (ii) update, based on the primary features, an estimated pose of the mobile automation apparatus and a confidence level corresponding to the estimated pose; determine whether the confidence level exceeds a confidence threshold; when the confidence level does not exceed the threshold, switch to a secondary localization mode to: (i) detect secondary features in the captured image stream; (ii) update the estimated pose and the confidence level based on the secondary features; and (iii) search the image stream for the primary features; and responsive to detection of the primary features in the image stream, re-activate the primary localization mode. 
     Further examples disclosed herein are directed to a method in a navigational controller, the method comprising: controlling a camera of a mobile automation apparatus to capture a stream of images in a facility; updating an estimated pose of the mobile automation apparatus based on one of primary features or secondary features detected in the images; and selecting whether to detect the primary features or the secondary features in the images according to a confidence level associated with the estimated pose. 
       FIG.  1    depicts a mobile automation system  100  in accordance with the teachings of this disclosure. The system  100  includes a server  101  in communication with at least one mobile automation apparatus  103  (also referred to herein simply as the apparatus  103 ) and at least one client computing device  104  via communication links  105 , illustrated in the present example as including wireless links. In the present example, the links  105  are provided by a wireless local area network (WLAN) deployed via one or more access points (not shown). In other examples, the server  101 , the client device  104 , or both, are located remotely (i.e. outside the environment in which the apparatus  103  is deployed), and the links  105  therefore include wide-area networks such as the Internet, mobile networks, and the like. The system  100  also includes a dock  106  for the apparatus  103  in the present example. The dock  106  is in communication with the server  101  via a link  107  that in the present example is a wired link. In other examples, however, the link  107  is a wireless link. 
     The client computing device  104  is illustrated in  FIG.  1    as a mobile computing device, such as a tablet, smart phone or the like. In other examples, the client device  104  is implemented as another type of computing device, such as a desktop computer, a laptop computer, another server, a kiosk, a monitor, and the like. The system  100  can include a plurality of client devices  104  in communication with the server  101  via respective links  105 . 
     The system  100  is deployed, in the illustrated example, in a retail facility including a plurality of support structures such as shelf modules  110 - 1 ,  110 - 2 ,  110 - 3  and so on (collectively referred to as shelf modules  110  or shelves  110 , and generically referred to as a shelf module  110  or shelf  110 —this nomenclature is also employed for other elements discussed herein). Each shelf module  110  supports a plurality of products  112 . Each shelf module  110  includes a shelf back  116 - 1 ,  116 - 2 ,  116 - 3  and a support surface (e.g. support surface  117 - 3  as illustrated in  FIG.  1   ) extending from the shelf back  116  to a shelf edge  118 - 1 ,  118 - 2 ,  118 - 3 . 
     The shelf modules  110  are typically arranged in a plurality of aisles, each of which includes a plurality of modules  110  aligned end-to-end. In such arrangements, the shelf edges  118  face into the aisles, through which customers in the retail facility, as well as the apparatus  103 , may travel. As will be apparent from  FIG.  1   , the term “shelf edge”  118  as employed herein, which may also be referred to as the edge of a support surface (e.g., the support surfaces  117 ) refers to a surface bounded by adjacent surfaces having different angles of inclination. In the example illustrated in  FIG.  1   , the shelf edge  118 - 3  is at an angle of about ninety degrees relative to the support surface  117 - 3  and to the underside (not shown) of the support surface  117 - 3 . In other examples, the angles between the shelf edge  118 - 3  and the adjacent surfaces, such as the support surface  117 - 3 , is more or less than ninety degrees. 
     The apparatus  103  is equipped with a plurality of navigation and data capture sensors  108 , such as image sensors (e.g. one or more digital cameras) and depth sensors (e.g. one or more Light Detection and Ranging (LIDAR) sensors, one or more depth cameras employing structured light patterns, such as infrared light, or the like). The apparatus  103  is deployed within the retail facility and, via communication with the server  101  and use of the sensors  108 , navigates autonomously or partially autonomously along a length  119  of at least a portion of the shelves  110 . 
     While navigating among the shelves  110 , the apparatus  103  can capture images, depth measurements and the like, representing the shelves  110  (generally referred to as shelf data or captured data). Navigation may be performed according to a frame of reference  102  established within the retail facility. The apparatus  103  therefore tracks its pose (i.e. location and orientation) in the frame of reference  102 . The process of updating the current pose of the apparatus  103  relative to the frame of reference  102  is also referred to as localization. As will be discussed below, the apparatus  103  implements a plurality of localization modes, and switches between those localization modes under various conditions in order to maintain an accurate pose estimate under a wide variety of environmental and operational conditions. 
     The server  101  includes a special purpose controller, such as a processor  120 , specifically designed to control and/or assist the mobile automation apparatus  103  to navigate the environment and to capture data. The processor  120  is interconnected with a non-transitory computer readable storage medium, such as a memory  122 , having stored thereon computer readable instructions for performing various functionality, including control of the apparatus  103  to navigate the modules  110  and capture shelf data, as well as post-processing of the shelf data. The memory  122  can also store data for use in the above-mentioned control of the apparatus  103 , such as a repository  123  containing a map of the retail environment and any other suitable data (e.g. operational constraints for use in controlling the apparatus  103 , data captured by the apparatus  103 , and the like). 
     The memory  122  includes a combination of volatile memory (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor  120  and the memory  122  each comprise one or more integrated circuits. In some embodiments, the processor  120  is implemented as one or more central processing units (CPUs) and/or graphics processing units (GPUs). 
     The server  101  also includes a communications interface  124  interconnected with the processor  120 . The communications interface  124  includes suitable hardware (e.g. transmitters, receivers, network interface controllers and the like) allowing the server  101  to communicate with other computing devices—particularly the apparatus  103 , the client device  104  and the dock  106 —via the links  105  and  107 . The links  105  and  107  may be direct links, or links that traverse one or more networks, including both local and wide-area networks. The specific components of the communications interface  124  are selected based on the type of network or other links that the server  101  is required to communicate over. In the present example, as noted earlier, a wireless local-area network is implemented within the retail facility via the deployment of one or more wireless access points. The links  105  therefore include either or both wireless links between the apparatus  103  and the mobile device  104  and the above-mentioned access points, and a wired link (e.g. an Ethernet-based link) between the server  101  and the access point. 
     The processor  120  can therefore obtain data captured by the apparatus  103  via the communications interface  124  for storage (e.g. in the repository  123 ) and subsequent processing (e.g. to detect objects such as shelved products in the captured data, and detect status information corresponding to the objects). The server  101  may also transmit status notifications (e.g. notifications indicating that products are out-of-stock, in low stock or misplaced) to the client device  104  responsive to the determination of product status data. The client device  104  includes one or more controllers (e.g. central processing units (CPUs) and/or field-programmable gate arrays (FPGAs) and the like) configured to process (e.g. to display) notifications received from the server  101 . 
     Turning now to  FIG.  2   , the mobile automation apparatus  103  is shown in greater detail. The apparatus  103  includes a chassis  201  containing a locomotive assembly  203  (e.g. one or more electrical motors driving wheels, tracks or the like). The apparatus  103  further includes a sensor mast  205  supported on the chassis  201  and, in the present example, extending upwards (e.g., substantially vertically) from the chassis  201 . The mast  205  supports the sensors  108  mentioned earlier. In particular, the sensors  108  include at least one imaging sensor  207 , such as a digital camera. In the present example, the mast  205  supports seven digital cameras  207 - 1  through  207 - 7  oriented to face the shelves  110 . The mast  205  also supports a ceiling-facing camera  208  having a field of view oriented upwards, towards a ceiling of the retail facility. As will be discussed in greater detail below, images captured by the camera  208  are employed by the apparatus  103  for localization. 
     The mast  205  also supports at least one depth sensor  209 , such as a  3 D digital camera capable of capturing both depth data and image data. The apparatus  103  also includes additional depth sensors, such as LIDAR sensors  211 . In the present example, the mast  205  supports two LIDAR sensors  211 - 1  and  211 - 2 . As shown in  FIG.  2   , the cameras  207  and the LIDAR sensors  211  are arranged on one side of the mast  205 , while the depth sensor  209  is arranged on a front of the mast  205 . That is, the depth sensor  209  is forward-facing (i.e. captures data in the direction of travel of the apparatus  103 ), while the cameras  207  and LIDAR sensors  211  are side-facing (i.e. capture data alongside the apparatus  103 , in a direction perpendicular to the direction of travel). In other examples, the apparatus  103  includes additional sensors, such as one or more RFID readers, temperature sensors, and the like. 
     The mast  205  also supports a plurality of illumination assemblies  213 , configured to illuminate the fields of view of the respective cameras  207 . That is, the illumination assembly  213 - 1  illuminates the field of view of the camera  207 - 1 , and so on. The sensors  207  and  211  are oriented on the mast  205  such that the fields of view of the sensors each face a shelf  110  along the length  119  of which the apparatus  103  is traveling. As noted earlier, the apparatus  103  is configured to track a pose of the apparatus  103  (e.g. a location and orientation of the center of the chassis  201 ) in the frame of reference  102 , permitting data captured by the apparatus  103  to be registered to the frame of reference  102  for subsequent processing. 
     Referring to  FIG.  3   , certain components of the mobile automation apparatus  103  are shown, in addition to the cameras  207  and  208 , depth sensor  209 , lidars  211 , and illumination assemblies  213  mentioned above. The apparatus  103  includes a special-purpose controller, such as a processor  300 , interconnected with a non-transitory computer readable storage medium, such as a memory  304 . The memory  304  includes a suitable combination of volatile memory (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor  300  and the memory  304  each comprise one or more integrated circuits. The memory  304  stores computer readable instructions for execution by the processor  300 . In particular, the memory  304  stores a localization application  308  which, when executed by the processor  300 , configures the processor  300  to perform various functions related to tracking the pose of the apparatus  103  within the facility. In particular, execution of the application  308  configures the processor  300  to maintain an updated pose estimate for the apparatus  103 , based on data received from the sensors  108 , and also to switch between the above-mentioned localization modes in update the pose estimate. 
     The processor  300 , when so configured by the execution of the application  308 , may also be referred to as a navigational controller  300 . Those skilled in the art will appreciate that the functionality implemented by the processor  300  via the execution of the application  308  may also be implemented by one or more specially designed hardware and firmware components, such as FPGAs, ASICs and the like in other embodiments. 
     The memory  304  may also store a repository  312  containing, for example, a map of the environment in which the apparatus  103  operates, for use during the execution of the application  308 . In addition, the apparatus  103  can (e.g. via execution of the application  308 ) update the map in the repository  312 , in a process referred to as simultaneous localization and mapping (SLAM). The apparatus  103  also includes a communications interface  316  enabling the apparatus  103  to communicate with the server  101  (e.g. via the link  105  or via the dock  106  and the link  107 ), for example to receive instructions to navigate to specified locations and initiate data capture operations. 
     In addition to the sensors mentioned earlier, the apparatus  103  includes a motion sensor  318 , such as one or more wheel odometers coupled to the locomotive assembly  203 . The motion sensor  318  can also include, in addition to or instead of the above-mentioned wheel odometer(s), an inertial measurement unit (IMU) configured to measure acceleration along a plurality of axes. 
       FIG.  3    also illustrates certain components of the application  308 . As will be apparent to those skilled in the art, the components of the application  308  can also be implemented as a suite of distinct applications, or in other subcombinations than that shown in  FIG.  3   . 
     The application  308 , in the present example, includes a data capture controller  320  that controls the sensors of the apparatus  108  to obtain data for subsequent processing by the remaining components of the application  308 . For example, the data capture controller  320  controls the camera  208  to capture a stream of images, and can also control the motion sensor to capture wheel odometry data, acceleration measurements, or the like. 
     The data obtained by the data capture controller  320  is provided to either or both of a pose estimator  324  and a set of feature detectors  328 . The pose estimator  324  executes any suitable mechanism, or combination of mechanisms, to generate an estimated pose of the apparatus  103  according to the frame of reference  102 , as well as a confidence level associated with the estimated pose. The pose estimator  324  can generate the estimated pose and confidence directly from sensor data received from the data capture controller  320  under some conditions. Under other conditions, the pose estimator  324  generates the pose and confidence based on features detected in the sensor data by the feature detectors  328 . Two feature detectors  328 - 1  and  328 - 2  are shown in the illustrated example, but in other embodiments a greater number of feature detectors  328  may be implemented. The pose estimator  324  also controls the activity of the feature detectors  328 , as will be discussed in greater detail below, enabling or disabling the feature detectors  328  depending on the active localization mode. 
     The actions performed by the apparatus  103 , and specifically by the processor  300  as configured via execution of the application  308 , to maintain an updated pose estimate of the apparatus in the frame of reference  102  will now be discussed in greater detail, with reference to  FIG.  4   .  FIG.  4    illustrates a method  400  of adaptive ceiling-based localization, which will be described in conjunction with its performance in the system  100 , and in particular by the apparatus  103 , with reference to the components illustrated in  FIGS.  2  and  3   . As will be apparent in the discussion below, in other examples, some or all of the processing performed by the server  101  may be performed by the apparatus  103 , and some or all of the processing performed by the apparatus  103  may be performed by the server  101 . 
     In general, via the performance of the method  400 , the apparatus  103  implements at least two localization modes (three modes are discussed in the example below). In each localization mode, the apparatus  103  detects distinct types of features present in the retail facility from captured sensor data, and updates the estimated pose of the apparatus  103  based on the detected features. The apparatus  103  additionally monitors certain conditions and switches between localization modes when such conditions are met. 
     Beginning at block  405 , the apparatus  103  initiates capture of an image stream via the camera  208 . For example, the data capture controller  320  shown in  FIG.  3    can control the camera  208  to begin capturing a stream of images at any suitable rate, e.g. 30 frames per second. The captured images can be stored in the memory  304 , and are obtained by the data capture controller  320  for further processing by the other components of the application  308 . The capture of images continues throughout the performance of the method  400  as described below. 
     At block  410 , the apparatus  103  performs localization via the detection of primary features in the images captured by the camera  208 . In particular, the pose estimator  324  activates the primary feature detector  328 - 1 . The primary feature detector  328 - 1 , in turn, searches the image stream initiated at block  405  for primary features. Various forms of primary features are contemplated. In the present example, the primary features are corner-point features, detected via any suitable feature-tracking mechanism. 
     An example of such a mechanism, which also enables the application  308  to update the map in the repository  312  simultaneously with the localization functions discussed herein (i.e. which enables the apparatus  103  to perform SLAM), is ORB SLAM. In such an implementation, the primary feature detector  328 - 1  is an Oriented FAST and rotated BRIEF (ORB) feature detector configured to detect the positions of salient points in successive images of the image stream. Various other examples of SLAM mechanisms and feature detectors will be apparent to those skilled in the art. 
     Turning to  FIG.  5   , a side view of the apparatus  103  is shown adjacent to a shelf module  110 . A ceiling  500  of the facility is also shown, and a field of view  504  of the camera  208  is illustrated. As shown in  FIG.  5   , various physical features present on the ceiling  500  are captured by the camera  208 . An example image  508  captured at the position shown in  FIG.  5    is illustrated in the upper portion of  FIG.  5   . As illustrated, the ceiling supports lamps in the form of bulb lamps  512  and linear lamps  516 , as well as a sign  520  bearing text and/or other indicia. As shown in the image, the ceiling  500  may also include ridged elements  524  such as a support beams or the like. The lamps  512  and  516  are characterized by greater contrast relative to the ceiling  500  than the sign  520  as a result of the light emitted by the lamps  512  and  516 . However, the lamps  512  typically lack clearly detectable edges or corners in images. The lamps  516  may be detectable as edge features (i.e. lines), but typically lack clearly detectable corner features in images. Further, the lamps  512  and  516  may occur in repeating patterns along the ceiling  500 , complicating the task of determining the location of any individual lamp  512  or  516 . The sign  520 , on the other hand, includes readily detectable edges and corners, including for example the point  528  which may be identified by the primary feature detector  328 - 1 . Various other features may also be detected by the primary feature detector  328 - 1  from the image  508 . 
     Returning to  FIG.  4   , at block  410  the apparatus  103  therefore generates an estimated pose and a confidence level by tracking the point  528  (and any other primary features, such as other corners of the sign  520 , corners of the indicia on the sign  520 , and the like) over successive frames in the image stream. The estimated pose and confidence level may also be generated with reference to the map stored in the repository  312 , if the map contains an indication of the point  528 . Alternatively, as noted above, in a SLAM process the point  528  may be added to the map for subsequent use in localization of the apparatus  103 . 
     Referring again to  FIG.  5   , an estimated pose  532  of the apparatus  103  is illustrated over a portion of a map  534 . A confidence level  536  is also illustrated in connection with the pose  532 . The confidence level  536  is illustrated graphically as an ellipse indicating a degree of error in the pose  532 . The confidence level need not be expressed graphically, however. For example, the confidence level may be a score (e.g. a percentage) generated by the pose estimator  324  indicating a degree of certainty corresponding to the pose  532 . 
     At block  415 , the pose estimator  324  determines whether the confidence level associated with the current pose estimate (i.e. the confidence level  532 , in the illustrated example) exceeds a threshold. The threshold may be configurable, and may represent a level below which the estimated pose is considered insufficiently accurate for use in navigation. Assuming that, in the example illustrated in  FIG.  5   , the confidence level  532  exceeds the threshold, the pose estimator  324  proceeds to block  420 . At block  420 , the pose estimator  324  provides the estimated pose (and optionally the associated confidence level) to another suitable process executed by the processor  300 , such as a navigation module, for example to enable control of the locomotive assembly  203 . 
     The performance of the method  400  then returns to block  410 , for continued detection of primary features and updating of the current estimated pose and associated confidence level. As will now be apparent, as the apparatus  103  travels along the shelf module  110 , the position of the point  528  within successive images (i.e. captured after the image  508  shown in  FIG.  5   ) changes, and such change in position is employed to track changes in the pose of the apparatus  103 . The pose estimate can therefore continue to be updated based on detected primary features such as the point  528 , so long as the primary feature detector  328 - 1  is able to detect such features in successive images. Under some conditions, the primary feature detector  328 - 1  may no longer be able to detect the point  528 , as discussed below. 
     Turning to  FIG.  6   , a further performance of blocks  410  and  415  is illustrated. As seen in  FIG.  6   , the apparatus  103  has traveled along the shelf module  110  and turned approximately 90 degrees to face away from the shelf module  110 . A further image  608  captured by the camera  208  is shown, illustrating that the rotation of the apparatus  103  has negatively impacted the quality of captured images. For example, the rotation of the apparatus  103  may introduce motion blur, and as a result the ridged elements  524  of the ceiling  500  are no longer visible, and the edges and indicia of the sign  520  may no longer be detectable in the image  608 . For example, the point  528  may be incorrectly detected at the location shown in  FIG.  6   , or may simply not be detected at all. Other environmental and operational conditions may also contribute to inaccurately detected primary features. Examples of such conditions include increases in a speed at which the apparatus  103  is travelling, insufficient lighting in the retail facility, and the like. 
     The pose estimate and confidence generated at block  415  may reflect the reduced accuracy of primary feature detection under such conditions. For example,  FIG.  6    illustrates an updated pose estimate  632  and a confidence level  636  indicating greater uncertainty than in the example of  FIG.  5   . It is assumed that the confidence level  636  does not exceed the threshold assessed at block  415 . Referring again to  FIG.  4   , the performance of the method  400  therefore proceeds to block  425 . 
     At block  425 , the pose estimator  324  switches from the primary localization mode implemented by blocks  410 - 420  into a secondary localization mode. In the secondary localization mode, the secondary feature detector  328 - 2  is enabled, and the apparatus therefore localizes based on secondary features detected in the image stream. The secondary features, in the present example, are the lamps  512  and  516 . As will be apparent, certain facilities may contain both linear and bulb lamps, while other facilities may contain only linear lamps, or only bulb lamps. The memory  304  can store a configuration setting indicating the types of lamps present in the facility, to reduce computational load on the processor  300  in facilities known to contain only one of the above types of lamps. 
     As noted earlier, the lamps  512  and  516  generally lack clearly detectable corner features, and the lamps  512  also generally lack detectable edge features. The lamps  512  and  516  may also appear in repeating patterns throughout the facility. These attributes may render the lamps  512  and  516  less effective for pose tracking than corner features such as the point  528  mentioned above. However, the lamps  512  and  516  are generally detectable in a wide range of environmental and operational conditions. For example, the lamps  512  and  516  may be less susceptible to motion blur caused by movement and/or rotation of the apparatus  103 . Specifically, the generally circular shape of the lamps  512  reduces the impact of motion blur in images of the lamps  512 . The lamps  516 , as a result of their readily detectable linear shapes and high contrast (when the lamps  516  are emitting light), may also be less susceptible to motion blur under at least some conditions (e.g. when the lamps  516  are at or near the center of the field of view of the camera  208 ). 
     The motion blur mentioned above may be caused by rapid movement of the apparatus  103 , rotation of the apparatus  103 , alone or in combination with insufficient shutter speed or exposure parameters of the camera  208 . The camera  208  may, for example, adjust such parameters too slowly to counteract changes in motion of the apparatus  103 . In other examples, the camera  208  may have fixed shutter speed and/or exposure settings. Further, in some examples the camera  208  may employ a fisheye lens, which captures images with distorted edges. The bulb lamps  512  in particular may be less susceptible to such distortion, remaining readily detectable by the secondary feature detector  328 - 1  over the full field of view  504  of the camera  208 . 
     The lamps  512  and  516  may be detected by the secondary feature detector  328 - 1  according to any suitable detection algorithms, including for example intensity-based algorithms suited to detecting the contrast between the lamps  512  and  516  and the ceiling  500 . Blob detection algorithms may be employed to detect the lamps  512 , while edge detection algorithms may be employed to detect the lamps  516 . Turning to  FIG.  7   , the image  608  is shown with example detected features  700  and  704  are shown. The feature  700  is a point feature marking the center of a bulb lamp  512 , and the feature  704  is a linear feature marking the centerline of a linear lamp  516 . Based on the positions of the features  700  and  704  within the image  608 , the pose estimator  324  generates a pose estimate  732  with a confidence level  736 . As seen by comparing  FIGS.  6  and  7   , the confidence level  736  is greater than the confidence level  636  as a result of the greater detectability of the lamps  512  and  516  under challenging imaging conditions. 
     Due to the repetitive nature of the lamps  512  and  516  in the facility, localization at block  425  can include the definition of a search area centered on the most recent pose estimate with a confidence level exceeding the threshold (e.g. the pose estimate  532  of  FIG.  5   ). The pose estimator  324  can then retrieve, from the map in the repository  312 , the positions of any lamps within the search area. By comparing the mapped lamp positions retrieved from the repository  312  with the detected lamp features (e.g.  700  and  704 ), the pose estimator  324  can then generate a pose estimate. The lamps  512  and  516  may, in other words, be used to regain a lost localization, despite their repetitive nature that renders the identification of specific lamps  512  and  516  in the image stream difficult. 
     Referring again to  FIG.  4   , at block  430  the pose estimator  324  determines whether the confidence level of the current pose estimate exceeds the threshold, as described above in connection with block  415 . In some examples, a different threshold (e.g. a lower threshold, permitting greater uncertainty in localization for the secondary localization mode) may be implemented at block  430 . In other examples, blocks  415  and  430  employ the same threshold. When the determination at block  430  is affirmative, the pose estimator outputs the pose estimate and confidence level at block  435 , as discussed above in connection with block  420 . 
     While localizing the secondary localization mode, the apparatus  103  continues to search the image stream for primary features. That is, during the determination of pose estimates based on the secondary features, the primary feature detector  328 - 1  remains active, and any detected primary features are provided to the pose estimator. At block  440 , the pose estimator  324  determines whether any primary features have been detected by the primary feature detector  328 - 1 . Detection of primary features when the secondary (i.e. lamp-based) localization mode is active may indicate a return to environmental or operational conditions that are favorable to the more accurate primary localization mode. When the determination at block  440  is affirmative, the pose estimator  324  therefore returns to block  410  (i.e. switches back to the primary localization mode). The secondary feature detector  328 - 2  is therefore disabled, and localization proceeds according to blocks  410 - 420  as discussed above. Thus, when the rotation of the apparatus  103  mentioned above ceases, the point  528  may once again be detectable on the sign  520 , and the primary localization mode may be reactivated. 
     Under certain conditions, the lamps  512  and  516  may also be rendered difficult to detect by the secondary feature detector  328 - 2 . For example, the lamps  512  and  516  may be turned off at certain hours of the day. Further, certain lamps may fail unpredictably, or the apparatus  103  may enter an area of the facility that does not contain ceiling-mounted lamps. Under such conditions, the secondary localization mode may also not generate a pose estimate with sufficiently high confidence. 
     For example, referring to  FIG.  8   , a further image  808  is shown as having been captured by the camera  208  after the lamps  512  and  516  have been disabled. As a result, the secondary feature detector  328 - 2  may be unable to detect any secondary features. Alternatively, the secondary feature detector  328 - 2  may detect secondary features inaccurately, as there is insufficient contrast to correctly identify the lamps  512  and  516  in the image  808 . The determination at block  430  is therefore negative, and the apparatus proceeds to block  445 . 
     At block  445 , the pose estimator  324  obtains odometry data directly from the data capture controller  320 , and generates a pose estimate and confidence level according to the odometry data alone. The performance of block  445  constitutes a backup localization mode. At block  450  the pose estimator  324  provides the pose estimate and associated confidence level for use by other components of the apparatus  103 . At block  455 , the pose estimator  324  then determines whether any secondary features have been detected. That is, in the backup localization mode, the secondary feature detector  328 - 2  remains active. The primary feature detector  328 - 1 , however, is disabled in the backup localization mode. When the determination at block  455  is negative, localization based on odometry data continues at block  445 . 
     When the determination at block  455  is affirmative, however (e.g. when the lamps  512  and  516  are turned back on), the pose estimator  324  returns to block  425 , and begins localizing based on the detected secondary features. As still now be apparent, when the pose estimator returns to block  425  from block  455 , the primary feature detector  328 - 1  is also enabled, to search for primary features that can be used to return to the primary localization mode. 
     Variations to the above systems and methods are contemplated. For example, in each of the primary and secondary localization modes noted above, odometry data may also be employed, for example by integrating odometry with visual features (whether primary or secondary) to generate the pose estimate. 
     In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings. 
     The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of any or all the claims. The invention is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued. 
     Moreover in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially”, “essentially”, “approximately”, “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed. 
     It will be appreciated that some embodiments may be comprised of one or more specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein. Alternatively, some or all functions could be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches could be used. 
     Moreover, an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation. 
     The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.