Informed, Constrained, Aligned: A Field Analysis on Degeneracy-aware Point Cloud Registration in the Wild
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Date
2025
Publication Type
Journal Article
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Abstract
The iterative closest point (ICP) registration algorithm has been a preferred method for light detection and ranging (LiDAR)-based robot localization for nearly a decade. However, even in modern simultaneous localization and mapping (SLAM) solutions, ICP can degrade and become unreliable in geometrically ill-conditioned environments. Current solutions primarily focus on utilizing additional sources of information, such as external odometry, to either replace the degenerate directions of the optimization solution or add additional constraints in a sensor-fusion setup afterward. In response, this work investigates and compares new and existing degeneracy mitigation methods for robust LiDAR-based localization and analyzes the efficacy of these approaches in degenerate environments for the first time in the literature at this scale. Specifically, this work investigates i) the effect of using active or passive degeneracy mitigation methods for the problem of ill-conditioned ICP in LiDAR degenerate environments and ii) the evaluation of truncated singular value decomposition (TSVD), inequality constraints (Ineq. Con.), and linear/nonlinear Tikhonov regularization for the application of degenerate point cloud registration for the first time. Furthermore, a sensitivity analysis for the least-squares minimization step of the ICP problem is carried out to better understand how each method affects the optimization and what to expect from each method. The results of the analysis are validated through multiple real-world robotic field and simulated experiments. The analysis demonstrates that active optimization degeneracy mitigation is necessary and advantageous in the absence of reliable external estimate assistance for LiDAR-SLAM, and soft-constrained methods can provide better results in complex ill-conditioned scenarios with heuristic fine-tuned parameters. The code and data used in this work are made publicly available to the community.
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published
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Journal / series
Volume
2
Pages / Article No.
485 - 515
Publisher
IEEE
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Subject
Point cloud compression; Optimization; Laser radar; Robots; Prevention and mitigation; Uncertainty; Simultaneous localization and mapping; Accuracy; Robustness; Pose estimation; Localization; Robust ICP; Field Robotics; LiDAR Degeneracy
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
Notes
Funding
852044 - Learning Mobility for Real Legged Robots (EC)
101016970 - Natural Intelligence for Robotic Monitoring of Habitats (EC)
227617 - 10000564 - Robot Orienteering in the Alps (SNF)
101016970 - Natural Intelligence for Robotic Monitoring of Habitats (EC)
227617 - 10000564 - Robot Orienteering in the Alps (SNF)