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.

翻译：激光雷达-相机融合是当前自动驾驶系统感知的核心过程之一。典型的传感器融合流程包含一系列遵循系统标定的坐标变换操作。尽管已有大量研究致力于提升融合精度，但在实际应用中，由于系统同步偏移、车辆振动、目标尺寸较小及相对运动速度较快等因素，仍存在固有的数据映射误差。此外，越来越多用于提高融合精度的复杂算法可能造成车载计算资源过载，从而限制实际部署。本研究提出一种新颖且低成本的概率性激光雷达-相机融合方法，以缓解场景重建中的这些固有映射误差。通过利用KL散度计算形状相似度，并应用基于RANSAC回归的轨迹平滑器，该方法在目标定位与距离估计中最小化了激光雷达-相机映射误差的影响。通过设计实验，验证了所提出策略的鲁棒性与有效性。