ENVIRONMENT RECONSTRUCTION AND PATH PLANNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.