End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization. (arXiv:2201.03860v1 [cs.RO])

Existing learning methods for LiDAR-based applications use 3D points scanned
under a pre-determined beam configuration, e.g., the elevation angles of beams
are often evenly distributed. Those fixed configurations are task-agnostic, so
simply using them can lead to sub-optimal performance. In this work, we take a
new route to learn to optimize the LiDAR beam configuration for a given
application. Specifically, we propose a reinforcement learning-based
learning-to-optimize (RL-L2O) framework to automatically optimize the beam
configuration in an end-to-end manner for different …

arxiv detection lidar localization optimization