CPC G01S 17/894 (2020.01) [G06T 7/50 (2017.01); G06T 15/08 (2013.01); G06V 10/454 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G01S 13/89 (2013.01); G01S 15/89 (2013.01); G01S 17/89 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/30261 (2013.01)] | 20 Claims |
1. A method of object detection, the method comprising:
receiving a first plurality of distance measurements from a first distance measurement sensor of a plurality of distance measurement sensors;
generating a first three-dimensional point cloud including a first plurality of points in a volume of space around the plurality of distance measurement sensors, wherein each of the first plurality of points in the first three-dimensional point cloud corresponds to one of the first plurality of distance measurements;
receiving a second plurality of distance measurements from a second distance measurement sensor of the plurality of distance measurement sensors;
generating a second three-dimensional point cloud including a second plurality of points in the volume of space around the plurality of distance measurement sensors, wherein each of the second plurality of points in the second three-dimensional point cloud corresponds to one of the second plurality of distance measurements;
generating a voxelized model of the volume of space based on a combination of the first three-dimensional point cloud and the second three-dimensional point cloud, the voxelized model including a plurality of voxels positioned based on the first plurality of points and the second plurality of points; and
identifying an object within the voxelized model by classifying a cluster of voxels using a model, wherein at least a portion of the voxelized model, which is missing one or more points from at least the first three-dimensional point cloud or the second three-dimensional point cloud is filled in with voxels based on the classification of the cluster of voxels.
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