Informed, Constrained, Aligned: A Field Analysis on Degeneracy-Aware Point Cloud Registration in the Wild (I)

Abstract

The iterative closest point 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. 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.