An Efficient Probabilistic Solution to Mapping Errors in LiDAR-Camera Fusion for Autonomous Vehicles.

LiDAR-camera fusion is one of the core processes for the perception system of current automated driving systems. The typical sensor fusion process includes a list of coordinate transformation operations following system calibration. Although a significant amount of research has been done to improve the fusion accuracy, there are still inherent data mapping errors in practice related to system synchronization offsets, vehicle vibrations, the small size of the target, and fast relative moving speeds. Moreover, more and more complicated algorithms to improve fusion accuracy can overwhelm the onboard computational resources, limiting the actual implementation. This study proposes a novel and low-cost probabilistic LiDAR-Camera fusion method to alleviate these inherent mapping errors in scene reconstruction. By calculating shape similarity using KL-divergence and applying RANSAC-regression-based trajectory smoother, the effects of LiDAR-camera mapping errors are minimized in object localization and distance estimation. Designed experiments are conducted to prove the robustness and effectiveness of the proposed strategy.