Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization

2024-10-14

IEEE Robotics and Automation Letters

AI Summary

LiDAR localization and mapping suffer from inaccurate pose estimation in geometrically degenerate environments—such as textureless regions or parallel surfaces—due to failure of point-to-plane optimization. To address this, we propose a probabilistic degeneration detection method based on noise propagation, which jointly models uncertainty in both point positions and surface normals, and integrates this into Hessian matrix uncertainty analysis. This enables real-time identification of degenerate directions and adaptive attenuation of pose updates. Our approach is interpretable and supports datasheet-driven quantification of degeneration probability. Evaluated on four real-world datasets, the method significantly improves registration robustness and accuracy over state-of-the-art approaches, particularly in severely degenerate scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping. This work introduces a new probabilistic method to detect and mitigate the effect of degeneracies in point-to-plane error minimization. The noise on the Hessian of the point-to-plane optimization problem is characterized by the noise on points and surface normals used in its construction. We exploit this characterization to quantify the probability of a direction being degenerate. The degeneracy-detection procedure is used in a new real-time degeneracy-aware iterative closest point algorithm for LiDAR registration, in which we smoothly attenuate updates in degenerate directions. The method's parameters are selected based on the noise characteristics provided in the LiDAR's datasheet. We validate the approach in four real-world experiments, demonstrating that it outperforms state-of-the-art methods at detecting and mitigating the adverse effects of degeneracies.

Problem

• LiDAR
• Point-to-Plane Errors
• Orientation Issues

Innovation

• Probabilistic Method
• LiDAR Mapping
• Noise Reduction