用向量场可视化激光雷达点云差异，辅助识别扫描匹配问题
Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization
- 通过向量场展现两组点云的局部偏差模式
- 人类分析员可据此逐步定位并剔除干扰机制
- 适合离线分析激光雷达数据异常的工程师
本文提出一种可视化方法，帮助人工分析师分类影响激光雷达扫描匹配的不利因素。该方法适用于离线分析而非实时场景，生成向量场图以表征一对配准点云之间的局部差异。向量场能揭示原始点云数据中难以察觉的模式。我们通过两个概念验证案例验证该方法：一个为仿真研究，另一个为实地实验。在两个数据集上，人工分析师均能推断出一系列不利机制，并逐次从原始数据中移除这些机制，从而聚焦于越来越小的残差差异。
原文摘要 · Abstract (English)
In this paper we introduce a visualization methodology to aid a human analyst in classifying adversity modes that impact lidar scan matching. Our methodology is intended for offline rather than real-time analysis. The method generates a vector-field plot that characterizes local discrepancies between a pair of registered point clouds. The vector field plot reveals patterns that would be difficult for the analyst to extract from raw point-cloud data. After introducing our methodology, we apply the process to two proof-of-concept examples: one a simulation study and the other a field experiment. For both data sets, a human analyst was able to reason about a series of adversity mechanisms and iteratively remove those mechanisms from the raw data, to help focus attention on progressively smaller discrepancies.
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