Reliable position and attitude sensing is critical for highly automated vehicles that operate on conventional roadways. Lidar sensors are increasingly incorporated into pose-estimation systems. Despite its great utility, lidar is a complex sensor, and its performance in roadway environments is not yet well understood. For instance, it is often assumed in lidar-localization algorithms that a lidar will always identify a unique surface along a given raypath. However, this assumption is not always true, as ample prior evidence exists to suggest that lidar units may generate measurements probabilistically when more than one scattering surface appears within the lidar's conical beam. In this paper, we analyze lidar datasets to characterize cases with probabilistic returns along particular raypaths. Our contribution is to present representative cumulative distribution functions (CDFs) for raypaths observed by two different mechanically rotating lidar units with stationary bases. In subsequent discussion, we outline a qualitative methodology to assess the effect of probabilistic multi-return cases on lidar-based localization.

翻译：可靠的位置和姿态感知对于在常规道路上运行的高度自动化车辆至关重要。激光雷达传感器正越来越多地应用于姿态估计系统中。尽管激光雷达具有巨大效用，但作为一种复杂传感器，其在道路环境中的性能尚未得到充分理解。例如，在激光雷达定位算法中通常假设激光雷达总能沿给定射线路径识别出唯一的散射表面。然而，这一假设并非始终成立，因为已有充分先验证据表明，当多个散射表面出现在激光雷达锥形波束范围内时，激光雷达单元可能以概率方式生成测量值。本文通过分析激光雷达数据集，对沿特定射线路径出现概率性回波的场景进行表征。我们的贡献在于，针对两台静止底座、机械旋转式激光雷达所观测的射线路径，给出了代表性的累积分布函数（CDF）。在后续讨论中，我们概述了一种定性方法论，用以评估概率性多重回波情况对基于激光雷达的定位系统的影响。