X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
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Author / Producer
Date
2024
Publication Type
Journal Article
ETH Bibliography
yes
OPEN ACCESS
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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.
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Publication status
published
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Book title
Journal / series
Volume
40
Pages / Article No.
452 - 471
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Constrained iterative closest point (ICP); environment degeneracy; lidar localizability; optimization degeneracy; robust localization
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
Notes
EU Horizon 2020 programme grant agreement No. 101070405 ;
EU Horizon 2021 programme grant agreement No. 101070596 ;
The NCCR digital fabrication and robotics ;
The ETH Zurich Research Grant No. 21-1 ETH-27
Funding
852044 - Learning Mobility for Real Legged Robots (EC)
101016970 - Natural Intelligence for Robotic Monitoring of Habitats (EC)
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)
101016970 - Natural Intelligence for Robotic Monitoring of Habitats (EC)
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)