Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization

Daniel Choate and Jason H. Rife

Peer Reviewed

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.
Published in: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024)
September 16 - 20, 2024
Hilton Baltimore Inner Harbor
Baltimore, Maryland
Pages: 1771 - 1784
Cite this article: Choate, Daniel, Rife, Jason H., "Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 1771-1784. https://doi.org/10.33012/2024.19864
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