CPC G01S 7/2813 (2013.01) [G01S 13/865 (2013.01); G01S 13/9011 (2013.01); G01S 13/9017 (2013.01); G01S 13/931 (2013.01)] | 20 Claims |
1. A computer-implemented method of sensor output segmentation, the computer-implemented method comprising:
accessing sensor data comprising a plurality of sensor data returns representative of an environment detected by at least one sensor across a field of view of the at least one sensor;
associating the plurality of sensor data returns with a plurality of bins corresponding to the field of view of the at least one sensor, wherein the plurality of bins respectively correspond to a different angular portion associated with a number of degrees of the field of view of the at least one sensor;
for a bin of the plurality of bins that contains multiple sensor data returns respectively indicating objects at multiple different distances, selecting a sensor data return of the multiple sensor data returns in the bin, the selected sensor data return being characterized by a minimum distance of the multiple different distances;
generating a plurality of channels for the plurality of bins, the plurality of channels respectively comprising data indicative of a range and an azimuth associated with at least one sensor data return associated with a respective bin of the plurality of bins, and wherein the plurality of channels further comprises data indicative of a signal to noise ratio associated with the sensor data of respective bins of the plurality of bins; and
generating a semantic segment of at least a portion of the sensor data representative of the environment by inputting the plurality of channels into a machine-learned segmentation model trained to segment at least a portion of the plurality of sensor data returns based at least in part on input comprising the plurality of channels, wherein the machine-learned segmentation model generates at least one output comprising the semantic segment.
|