  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.
