X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments

Yoshua Nava, Turcan Tuna, Julian Nubert, Marco Hutter, Shehryar Khattak

Abstract:

Modern robotic systems are required to operate in
challenging environments, which demand reliable localization
under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are
known to deteriorate point cloud registration performance and
push optimization toward divergence along weakly constrained
directions. To overcome this issue, this work proposes i) a
robust fine-grained localizability detection module, and ii) a
localizability-aware constrained ICP optimization module, which
couples with the localizability detection module in a unified
manner. The proposed localizability detection is achieved by
utilizing the correspondences between the scan and the map
to analyze the alignment strength against the principal directions of the optimization as part of its fine-grained LiDAR
localizability analysis. In the second part, this localizability
analysis is then integrated into the scan-to-map point cloud
registration to generate drift-free pose updates by enforcing
controlled updates or leaving the degenerate directions of the
optimization unchanged. The proposed method is thoroughly
evaluated and compared to state-of-the-art methods in simulated
and real-world experiments1
, demonstrating the performance and
reliability improvement in LiDAR-challenging environments. In
all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without environment-specific parameter tuning.