Efficient High-Speed Parallel Compressive LiDAR

Advances in solid-state photon detector arrays offer the potential of high-performance LiDAR systems but require novel signal processing approaches to fully exploit the dramatic increase in data volume an arrayed detector can provide to enable advanced autonomous decision making e.g. for self-driving cars. We present a novel block compressive sensing framework to enable efficient low power sparse illumination for long range applications at high framerates supported by a system hardware study to further reduce complexity and resources of the proposed frameworks with application of precision scaling to the problem to accelerate parallel compressive depth imaging.