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.
@article{arxiv.2510.13619,
title = {Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization},
author = {Daniel Choate and Jason Rife},
journal= {arXiv preprint arXiv:2510.13619},
year = {2025}
}
Comments
This is the preprint version of the paper published in: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), September 2024 The final version is available at https://doi.org/10.33012/2024.19864