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arXiv · 2510.13619

Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization

Daniel Choate·Jason Rife
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Abstract

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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Daniel Choate, Jason Rife. 2025-10-15. Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization. https://doi.org/10.33012/2024.19864

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