Online Geometric Consistency Assessment of LiDAR Measurements via Recursive Bayesian Comparison
Overview
This paper presents a recursive Bayesian framework for online assessment of LiDAR measurement consistency using accumulated geometric history. By coupling a Normal-Inverse-Wishart model with a weak isotropic reference, the method computes Normalized Drift Evidence (NDE) to distinguish corrupted from consistent sequences without an independent external reference. Evaluated across 14 real-world sequences totaling approximately 73 minutes from four platform types and ten UAV trials, the approach correctly identified all nine corrupted sequences while remaining silent on five consistent ones, with alarms occurring within 0.9 seconds of event references in representative scenarios. The system processes updates at 1 Hz with a mean latency of 4.2–6.1 ms per update and enables autonomous responses such as odometry isolation, landing, relocalization, and mission continuation upon detection.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · elapsed_minutes: 73 other · reported trials
“omparing the marginal predictive support of the same current points under both models yields Normalized Drift Evidence (NDE). Assessment precedes assimilation: positive NDE triggers an alarm and preserves the preceding historical state; otherwise, the current observations update the history. Across 14 real-world sequences totaling approximately 73 min from four platform types, alarms identified all nine corrupted seq”
Exact source · revision 1Source owner not reported
Reported quantity · processing_time_ms: 4.2 other · Basis not reported · Differing source assertions
“ic support. Mean processing time is 4.2--6.1 ms per update at 1 Hz. In ten UAV trials, alarms initiated odometry isolation, landing, relocalization, and mission continuation.”
Exact source · revision 1Source owner not reported
Reported quantity · processing_time_ms: 6.1 other · Basis not reported · Differing source assertions
“ic support. Mean processing time is 4.2--6.1 ms per update at 1 Hz. In ten UAV trials, alarms initiated odometry isolation, landing, relocalization, and mission continuation.”
Exact source · revision 1Source owner not reported
Reported quantity · elapsed_seconds: 0.9 other · reported trials
“uences and were absent from all five consistent sequences. In four representative scenarios with event references, first alarms occurred within 0.9 s of the reference times, with no preceding alarms. Cross-backend evaluation demonstrates consistent responses to reconstructed geometry. Ablation and controlled perturbation results establish the roles of uncertainty modeling, recursive history, displacement, and geometr”
Exact source · revision 1Source owner not reported
Reported quantity · sequences: 14 other · reported trials
“omparing the marginal predictive support of the same current points under both models yields Normalized Drift Evidence (NDE). Assessment precedes assimilation: positive NDE triggers an alarm and preserves the preceding historical state; otherwise, the current observations update the history. Across 14 real-world sequences totaling approximately 73 min from four platform types, alarms identified all nine corrupted seq”
Exact source · revision 1Source owner not reported
Reported quantity · corrupted_sequences: 9 other · reported trials
“omparing the marginal predictive support of the same current points under both models yields Normalized Drift Evidence (NDE). Assessment precedes assimilation: positive NDE triggers an alarm and preserves the preceding historical state; otherwise, the current observations update the history. Across 14 real-world sequences totaling approximately 73 min from four platform types, alarms identified all nine corrupted seq”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalOnline Geometric Consistency Assessment of LiDAR Measurements via Recursive Bayesian Comparison
This paper presents a recursive Bayesian framework for online assessment of LiDAR measurement consistency using accumulated geometric history. By coupling a Normal-Inverse-Wishart model with a weak isotropic reference, the method computes Normalized Drift Evidence (NDE) to distinguish corrupted from consistent sequences without an independent external reference. Evaluated across 14 real-world sequences totaling approximately 73 minutes from four platform types and ten UAV trials, the approach correctly identified all nine corrupted sequences while remaining silent on five consistent ones, with alarms occurring within 0.9 seconds of event references in representative scenarios. The system processes updates at 1 Hz with a mean latency of 4.2–6.1 ms per update and enables autonomous responses such as odometry isolation, landing, relocalization, and mission continuation upon detection.
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