Patent Publication Number: US-2023147739-A1

Title: Automatic detection of lidar to vehicle alignment state using localization data

Description:
INTRODUCTION 
     The subject disclosure relates to automatic detection of lidar to vehicle alignment state using localization data. 
     Vehicles (e.g., automobiles, trucks, construction equipment, farm equipment) increasingly include sensors that obtain information about the vehicle and its environment. The information facilitates semi-autonomous or autonomous operation of the vehicle. For example, sensors (e.g., camera, radar system, lidar system, inertial measurement unit (IMU), steering angle sensor) may facilitate semi-autonomous maneuvers such as automatic braking, collision avoidance, or adaptive cruise control. Generally, sensors like cameras, radar systems, and lidar systems have a coordinate system that differs from the vehicle coordinate system. The sensor coordinate system must be properly aligned with the vehicle coordinate system to obtain information from the sensor that is easily applicable to vehicle operation. Accordingly, it is desirable to provide automatic detection of lidar to vehicle alignment state using localization data. 
     SUMMARY 
     In one exemplary embodiment, a system in a vehicle includes a lidar system to obtain lidar data in a lidar coordinate system and processing circuitry to obtain the lidar data and localization data. The localization data indicates a location and orientation of the vehicle. The processing circuitry automatically determines an alignment state resulting in a lidar-to-vehicle transformation matrix that projects the lidar data from the lidar coordinate system to a vehicle coordinate system to provide lidar-to-vehicle data. The alignment state is determined using the localization data. 
     In addition to one or more of the features described herein, the processing circuitry obtains a vehicle-to-world transformation matrix to project data from the vehicle coordinate system to a world coordinate system, which is a fixed coordinate system, based on the localization. 
     In addition to one or more of the features described herein, the processing circuitry obtains a plurality of frames of lidar data at corresponding time stamps, determines transformation matrices, each transformation matrix being between a first of the plurality of frames of lidar data and a subsequent one of the plurality of frames of lidar data, and obtains a residual sum based on the transformation matrices, lidar-to-vehicle transformation matrix for each frame, and the vehicle-to-world transformation matrix for each frame. 
     In addition to one or more of the features described herein, the processing circuitry determines the alignment state as aligned based on an average of the residual sum for each frame being less than or equal to a threshold value. 
     In addition to one or more of the features described herein, the processing circuitry aggregates a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, to use the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, and to identify at least a predefined minimum number of objects. 
     In addition to one or more of the features described herein, the processing circuitry performs principal component analysis or a determination of density for points of the aggregated lidar data based on a type of object among the at least the predefined minimum number of objects associated with the points and determines the alignment state based on a minimum eigenvalue resulting from the principal component analysis or the density. 
     In addition to one or more of the features described herein, the processing circuitry aggregates a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, uses the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, and identifies objects in the aggregated lidar data in the world coordinate system. 
     In addition to one or more of the features described herein, the processing circuitry obtains a high definition map, a pre-saved point cloud indicating stationary objects, or obtains vehicle-to-everything (V2X) communication, identifies one or more objects as ground truth objects, and determines the alignment state based on a distance between the objects identified in the aggregated lidar data and corresponding ones of the ground truth objects. 
     In addition to one or more of the features described herein, the processing circuitry trains a deep learning neural network based on collecting lidar data, localization data, and an aligned lidar-to-vehicle transformation matrix, injecting different levels of alignment fault into the collected lidar data to generate modified lidar data and labeling the modified lidar data according to the alignment fault, and implements supervised learning to train the deep learning neural network to classify alignment as good or faulty. 
     In addition to one or more of the features described herein, the processing circuitry aggregates a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, uses the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, implements the deep learning neural network, and determines the alignment state based on the indication of the alignment as good or faulty. 
     In another exemplary embodiment, a method in a vehicle includes obtaining, by processing circuitry from a lidar system, lidar data in a lidar coordinate system, and obtaining, by the processing circuitry, localization data, wherein the localization data indicates a location and orientation of the vehicle. The method also includes automatically determining, by the processing circuitry, an alignment state resulting in a lidar-to-vehicle transformation matrix that projects the lidar data from the lidar coordinate system to a vehicle coordinate system to provide lidar-to-vehicle data. The alignment state is determined using the localization data. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry obtaining a vehicle-to-world transformation matrix to project data from the vehicle coordinate system to a world coordinate system, which is a fixed coordinate system, based on the localization. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry obtaining a plurality of frames of lidar data at corresponding time stamps, determining transformation matrices, each transformation matrix being between a first of the plurality of frames of lidar data and a subsequent one of the plurality of frames of lidar data, and obtaining a residual sum based on the transformation matrices, lidar-to-vehicle transformation matrix for each frame, and the vehicle-to-world transformation matrix for each frame. 
     In addition to one or more of the features described herein, the determining the alignment state includes determining that the alignment state is aligned based on an average of the residual sum for each frame being less than or equal to a threshold value. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry aggregating a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, using the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, and identifying at least a predefined minimum number of objects. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry performing principal component analysis or a determination of density for points of the aggregated lidar data based on a type of object among the at least the predefined minimum number of objects associated with the points and determining the alignment state based on a minimum eigenvalue resulting from the principal component analysis or the density. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry aggregating a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, using the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, and identifying objects in the aggregated lidar data in the world coordinate system. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry obtaining a high definition map, a pre-saved point cloud indicating stationary objects, or vehicle-to-everything (V2X) communication, identifying one or more objects as ground truth objects, and determining the alignment state based on a distance between the objects identified in the aggregated lidar data and corresponding ones of the ground truth objects. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry training a deep learning neural network based on collecting lidar data, localization data, and an aligned lidar-to-vehicle transformation matrix, injecting different levels of alignment fault into the collected lidar data to generate modified lidar data and labeling the modified lidar data according to the alignment fault, and implementing supervised learning to train the deep learning neural network to classify alignment as good or faulty. 
     In addition to one or more of the features described herein, the method also includes the processing circuitry aggregating a plurality of frames of lidar data at corresponding time stamps to obtain aggregated lidar data, using the lidar-to-vehicle transformation matrix and the vehicle-to-world transformation matrix to obtain the aggregated lidar data in the world coordinate system, implementing the deep learning neural network, and determining the alignment state based on the indication of the alignment as good or faulty. 
     The above features and advantages, and other features and advantages of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Other features, advantages and details appear, by way of example only, in the following detailed description, the detailed description referring to the drawings in which: 
         FIG.  1    is a block diagram of a vehicle including automatic detection of lidar to vehicle alignment state according to one or more embodiments; 
         FIG.  2    is a process flow of a method of performing automatic detection of the alignment state between a lidar coordinate system and a vehicle coordinate system according to an exemplary embodiment; 
         FIG.  3    is a process flow of a method of performing automatic detection of the alignment state between a lidar coordinate system and a vehicle coordinate system according to another exemplary embodiment; 
         FIG.  4   . is a process flow of a method of performing automatic detection of the alignment state between a lidar coordinate system and a vehicle coordinate system according to another exemplary embodiment; 
         FIG.  5    is a process flow of a method of performing automatic detection of the alignment state between a lidar coordinate system and a vehicle coordinate system according to another exemplary embodiment; and 
         FIG.  6    is a process flow of a method of performing automatic detection of the alignment state between a lidar coordinate system and a vehicle coordinate system according to another exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features. 
     As previously noted, sensors like the lidar system have a coordinate system that is different than the vehicle coordinate system. Thus, information (e.g., location of objects around the vehicle) from the lidar system must be projected to the vehicle coordinate system through a transformation matrix in order to use the information to control vehicle operation in a straight-forward way. The transformation matrix is essentially a representation of the alignment between the two coordinate systems. That is, the alignment process is the process of finding the transformation matrix. Thus, the transformation matrix correctly projects the lidar information to the vehicle coordinate system when the two coordinate systems are properly aligned, and the transformation matrix does not project the lidar information to the vehicle coordinate system correctly when the two coordinate systems are misaligned. Knowing the alignment state (i.e., aligned or misaligned) is important for correcting the transformation matrix as needed. Further, monitoring the alignment state over time (e.g., dynamically detecting the alignment state) is important because ageing, vibration, an accident, or other factors may change the alignment state. 
     A prior approach to ensuring alignment between the lidar system and the vehicle involves manually observing lidar point clouds in the lidar coordinate system and those same lidar point clouds projected to the vehicle coordinate system to determine if there is a misalignment in the transformation matrix that is visible in the projected lidar point clouds. This approach has several drawbacks including the time required and the fact that the assessment does not lend itself to being performed in real-time during vehicle operation. Embodiments of the systems and methods detailed herein relate to automatic detection of lidar to vehicle alignment state (i.e., aligned or misaligned) using localization data. Localization data indicates the location and orientation of the vehicle. 
     In accordance with an exemplary embodiment,  FIG.  1    is a block diagram of a vehicle  100  including automatic detection of lidar to vehicle alignment state. Detecting the alignment state refers to determining whether the existing transformation matrix projects data from the lidar coordinate system  115  to the vehicle coordinate system  105  correctly (i.e., alignment state is aligned) or not (i.e., alignment state is misaligned). The exemplary vehicle  100  shown in  FIG.  1    is an automobile. The vehicle  100  is shown to include a lidar system  110  that has the lidar coordinate system  115 . The world coordinate system  102  is shown in addition to the vehicle coordinate system  105  and lidar coordinate system  115 . The world coordinate system  102  is unchanging while the other coordinate systems  105 ,  115  may shift with the motion of the vehicle  100 . Three exemplary objects  140   a ,  140   b ,  140   c  (generally referred to as  140 ) are shown. Object  140   a  is a light pole, and object  140   b  is a traffic sign. Object  140   c  may be another car or a pedestrian, for example. 
     While one lidar system  110  is shown, the exemplary illustration is not intended to be limiting with respect to the numbers or locations of lidar systems  110 . The vehicle  100  may include any number of lidar systems  110  or other sensors  120  (e.g., camera, radar systems, IMU  126 , global navigation satellite system (GNSS) such as global positioning system (GPS)  125 ) at any location around the vehicle  100 . The other sensors  120  may provide localization information (e.g., location and orientation of the vehicle  100 ), for example. The motions associated with yaw Y, pitch P, and roll R are indicated. A yaw angle is an angle between the direction of travel of the vehicle  100  and its x-axis xv and rotates around the z-axis vz of the vehicle  100 , while a pitch angle is relative to the y-axis vy of the vehicle  100  and a roll angle is relative to the z-axis vz of the vehicle  100  and rotates around the x-axis xv. This information may be obtained using other sensors  120  such as GPS  125  and IMU  126 . 
     In addition, the coordinate systems  105 ,  115  shown in  FIG.  1    are exemplary. Further, while three exemplary objects  140  are shown in  FIG.  1   , any number of objects  140  may be detected by one or more sensors. An exemplary lidar field of view (FOV)  111  is outlined. As previously noted, a transformation matrix facilitates the projection of data from one coordinate system to another. As also noted, the process of aligning two coordinate systems is the processing of determining the transformation matrix to project data from one coordinate system to the other. A misalignment refers to an error in that transformation matrix. 
     The vehicle  100  includes a controller  130  that controls one or more operations of the vehicle  100 . The controller  130  may perform the alignment process between the lidar system  110  and the vehicle  100  (i.e., determine the transformation matrix between the lidar coordinate system  115  and the vehicle coordinate system  105 ). The controller  130  may also perform communication with other vehicles in the form of vehicle-to-vehicle (V2V) messages, with infrastructure in the form of vehicle-to-infrastructure (V2I) messages, and with a cloud server or any other system, generally, via vehicle-to-everything (V2X) messages. This is further discussed with reference to  FIG.  5   . The controller  130  may additionally perform training and implementation of machine learning (e.g., a deep learning neural network). This is further discussed with reference to  FIG.  6   . 
     The controller  130  may additionally implement the processes discussed with reference to  FIGS.  2 - 6    to determine an alignment state between the lidar system  110  and the vehicle  100 . According to different exemplary embodiments, the controller  130  may implement one of the methods discussed with reference to  FIGS.  2 - 6    or may implement two or more of the methods in parallel. The controller  130  may include processing circuitry that may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
       FIG.  2    is a process flow of a method  200  of performing automatic detection of the alignment state between a lidar coordinate system  115  and a vehicle coordinate system  105  according to an exemplary embodiment. The embodiments discussed with reference to  FIGS.  2 - 6    rely on localization data (e.g., position and orientation of the vehicle  100 ) obtained in the world coordinate system  102 . This localization data may be obtained based on one of the other sensors  120  being a GPS  125 , for example. 
     At block  210 , the processes include obtaining lidar data from the lidar system  110  and localization data from a GPS  125  and IMU  126 , for example. At block  220 , a check is done to determine if an enabling condition is met. An enabling condition refers to a maneuver such as a U-turn, right turn, or left turn. The enabling condition may be quantified as one during which the yaw angle is greater than a predefined threshold (e.g., 90 degrees) and the translation of the vehicle (e.g., changes in xv and yv) is greater than a predefined threshold (e.g., 20 meters). If the check at block  220  indicates that enabling condition is not met, then the processes of obtaining lidar data and localization data (at block  210 ) continue. If, according to the check at block  220 , the enabling condition is met (i.e., the vehicle  100  is performing a maneuver that facilitates this alignment status determination according to the method  200 ), then the processes at block  230  are performed. 
     At block  230 , for a series of frames of lidar data (obtained at block  210  over a set of time stamps, indicated with index i), the transformation matrices Ai between the first frame and each of the subsequent frames are determined. This is done by known techniques referred to as lidar registration such as laser odometry and mapping (LOAM), iterative closest point (ICP), generalized ICP (GICP), and normal distributions transform (NDT). Outliers are then filtered out. That is, a smooth movement of the vehicle  100  is assumed during the enabling condition (e.g., U-turn). Thus, any one of the transformation matrices Ai that projects data outside a predefined range defined by an extrapolation result between frames is regarded as an outlier. Thus, after the outlier frames and corresponding ones of the transformation matrices Ai are removed, remaining transformation matrices Ai are retained. 
     Continuing the discussion of processes at block  230 , for the frames for which transformation matrices Ai are retained, vehicle-to-world transformation matrices T VWi  are obtained based on localization data. That is, yaw, pitch, and roll angles that make up the rotation matrix R and components Tx, Ty, Tz of a translation matrix T are obtained, for each frame indicated with index i, from other sensors  120  such as the GPS  125  and IMU  126 . Then, the vehicle-to-world transformation matrices T VWi  are obtained as: 
     
       
         
           
             
               
                 
                   
                     T 
                     VWi 
                   
                   = 
                   
                     [ 
                     
                       
                         
                           R 
                         
                         
                           T 
                         
                       
                       
                         
                           0 
                         
                         
                           1 
                         
                       
                     
                     ] 
                   
                 
               
               
                 
                   [ 
                   
                     EQ 
                     . 
                         
                     1 
                   
                   ] 
                 
               
             
           
         
       
     
     At block  240 , the lidar-to-vehicle transformation matrix T LV , which represents the alignment whose state is of interest, is used to calculate a residual sum S i  for each pair of transformation matrices Ai and T VWi : 
         S   i =( A   i   T   LV   −T   LV   T   VWi )  [EQ. 2]
 
     An average of the residual sums is also determined. At block  250 , a check is done of whether the average residual sum exceeds a predefined threshold. If it does, then a misalignment is reported as the alignment state, at block  260 . If, instead, the check at block  250  indicates that the average residual sum does not exceed the threshold, then alignment is determined as the alignment state, at block  270 . 
       FIG.  3    is a process flow of a method  300  of performing automatic detection of the alignment state between a lidar coordinate system  115  and a vehicle coordinate system  105  according to another exemplary embodiment. At block  310 , the processes include obtaining lidar data from the lidar system  110  and localization data, and, at block  320 , the processes include checking whether an enabling condition is met. These processes are similar to the processes at blocks  210  and  220 , discussed with reference to  FIG.  2   . If the check at block  320  indicates that enabling condition is not met, then the processes of obtaining lidar data and localization data (at block  310 ) continue. If, according to the check at block  320 , the enabling condition is met (i.e., the vehicle  100  is performing a maneuver that facilitates this alignment status determination according to the method  300 ), then the processes at block  330  are performed. 
     At block  330 , the processes include aggregating the lidar data obtained at block  310 , transforming the lidar data to the world coordinate system  102 , and identifying at least a minimum number N of objects  140  (e.g., N&gt;3) visible through the maneuver of the enabling condition identified at block  320 . The lidar data L (i.e., point cloud obtained by the lidar system  110 ) transformed to the world coordinate system  102  L W  is obtained as: 
         L   W   =T   VW   T   LV   L   [EQ. 3]
 
     As noted with reference to  FIG.  2   , T VW  is the vehicle-to-world transformation matrix and T LV  is the lidar-to-vehicle transformation matrix, which represents the alignment whose state is of interest. The aggregation at block  330  refers to concatenating the lidar data in the world coordinate system  102  L W . Identifying at least the minimum number N of objects  140  in the concatenated lidar data in the world coordinate system  102  L W  may entail using a known identification technique with some additional conditions. The conditions may include a minimum number of points of the point cloud, for example. Identifying the objects  140 , at block  330 , includes determining if each of the N objects  140  is a light pole (object  140   a ) or traffic sign (object  140   b ). 
     At block  340 , performing principal component analysis (PCA) is only done for points of the aggregated lidar data (i.e., point cloud) associated with each object  140  that is determined to be either a light pole (object  140   a ) or traffic sign (object  140   b ) at block  330 . Performing PCA on the points of the lidar data results in obtaining three eigenvalues corresponding to eigenvectors that indicate vagueness in the object  140 . At block  350 , a check is done of whether the minimum eigenvalue is less than a threshold. If it is, then alignment is determined as the alignment state, at block  380 . 
     If, on the other hand, the check at block  350  indicates that the minimum eigenvalue is not less than the threshold, then misalignment is reported as the alignment state, at block  390 . According to an exemplary embodiment, the outcome based on the check at block  350  may be sufficient. According to alternate embodiments, the processes at block  360  and the check at block  370  may be additionally or alternately performed. When the check at block  370  is additionally performed, a misalignment may only be reported, at block  390 , if both the checks and blocks  350  and  370  indicate misalignment (i.e., both checks reach block  390 ). 
     At block  360 , calculating point cloud density is done for points of the aggregated lidar data (i.e., point cloud) associated with each object  140  that is determined not to be a light pole (object  140   a ) or traffic sign (object  140   b ) at block  330 . Calculating the point cloud density may be performed according to different exemplary embodiments. According to an exemplary embodiment, a set (e.g., 20) of the nearest points to any point associated with the object  140  that is not a light pole (object  140   a ) or traffic sign (object  140   b ) may be selected. The density may be determined as the inverse of an average distance from the point to each of the set of the nearest points. According to another exemplary embodiments, the density may be calculated based on the number of other points within a distance (e.g., 1 meter) of the point associated with the object  140  that is not a light pole (object  140   a ) or traffic sign (object  140   b ). According to yet another exemplary embodiment, the density may be computed from the sum of the three eigenvalues obtained by performing a singular value decomposition of a set (e.g.,  20 ) of points associated with the object  140  that is not a light pole (object  140   a ) or traffic sign (object  140   b ). At block  370 , a check is done of whether the density is greater than a threshold density. If it is, then alignment is determined as the alignment state, at block  380 . If, on the other hand, the check at block  370  indicates that the density does not exceed the threshold density, then misalignment is reported as the alignment state, at block  390 . 
       FIG.  4    is a process flow of a method  400  of performing automatic detection of the alignment state between a lidar coordinate system  115  and a vehicle coordinate system  105  according to another exemplary embodiment. At block  410 , the processes include obtaining lidar data from the lidar system  110  and localization data from the GPS  125  and the IMU  126 , for example. At block  420 , the processes include aggregating the lidar data obtained at block  410 , transforming it to the world coordinate system according to EQ. 3, and loading a high definition (HD) map or pre-saved point cloud that represents ground truth and indicates stationary objects  140  in the current vicinity of the vehicle  100 . 
     At block  430 , the processes include identifying more than a minimum number N of objects  140  (e.g., N&gt;3) in the aggregated lidar data that are indicated on the HD map or in the pre-saved point cloud of the objects and calculating the distance between points of the lidar data corresponding to the objects  140  and associated points in the HD map or pre-saved point cloud. The distance may be calculated in terms of Euclidian distance, plane angle, or line orientation, for example. At block  440 , a check is done of whether the distances between any associated points exceed a threshold distance. If they do not, then alignment is determined as the alignment state, at block  460 . If, on the other hand, the check at block  440  indicates that the distance between any associated points exceeds the threshold distance, then misalignment is reported as the alignment state, at block  450 . 
       FIG.  5    is a process flow of a method  500  of performing automatic detection of the alignment state between a lidar coordinate system  115  and a vehicle coordinate system  105  according to another exemplary embodiment. At block  510 , the processes include obtaining lidar data from the lidar system  110  and localization data from the GPS  125 , for example. At block  520 , the processes include aggregating the lidar data obtained at block  510  and transforming it to the world coordinate system according to EQ. 3. 
     At block  530 , the processes include identifying and locating another vehicle (object  140 ) in the aggregated lidar data in the world coordinate system and also obtaining the location of the other vehicle via a V2X message. The processes at block  530  then include calculating the distance between the locations obtained via points of the lidar data corresponding to the other vehicle and via the V2X message. The distance may be calculated in terms of Euclidian distance, plane angle, or line orientation, for example. At block  540 , a check is done of whether the distance exceeds a threshold distance. If it does not, then alignment is determined as the alignment state, at block  560 . If, on the other hand, the check at block  540  indicates that the distance exceeds the threshold distance, then misalignment is reported as the alignment state, at block  550 . 
       FIG.  6    is a process flow of a method  600  of performing automatic detection of the alignment state between a lidar coordinate system  115  and a vehicle coordinate system  105  according to another exemplary embodiment. The method  600  requires processes (at blocks  603 ,  605 ,  607 ) to train a deep learning neural network. At block  603 , the processes include collecting lidar data and localization data when an enabling condition is satisfied (according to the discussion with reference to  FIG.  2  or  3   , for example) and additionally obtaining a ground truth transformation matrix (i.e., an aligned transformation matrix). At block  605 , the processes include injecting different levels of alignment fault (i.e., changing the aligned transformation matrix to different degrees), aggregating the transformed data, and labeling the data automatically according to the injected fault (e.g., aligned, not aligned). An exemplary alignment fault may involve adding 1 degree to the yaw, for example. At block  607 , the processes include implementing supervised learning to train the deep learning neural network to classify the alignment as good or faulty. Once the training is performed, the deep learning neural network may be implemented to automatically assess the state of alignment. That is, the training may be performed offline prior to implementation in the vehicle  100 . 
     At block  610 , the processes include obtaining lidar data from the lidar system  110  and localization data from the GPS  125  and the IMU  126 , for example. At block  615 , the processes include checking whether an enabling condition is met. These processes are similar to the processes at blocks  210  and  220 , discussed with reference to  FIG.  2   . At block  620 , the processes include aggregating the lidar data obtained at block  610  and transforming the aggregated lidar data to the world coordinate system using EQ. 3. At block  625 , applying the deep neural network that was trained according to the processes at blocks  603 ,  605 ,  607  facilitates obtaining an indication, at block  630 , of whether the alignment is good or faulty. If the alignment is indicated as faulty, at block  630 , then misalignment is reported as the alignment state, at block  635 . If, on the other hand, the alignment state is not indicated as faulty, at block  630 , then alignment is determined as the alignment state, at block  640 . 
     While the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from its scope. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiments disclosed, but will include all embodiments falling within the scope thereof