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Deskewed LiDAR Odometry for Quadruped Robots in Environments with Varying Elevation
1Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces a novel LiDAR odometry method for quadruped robots navigating challenging terrains. The approach significantly reduces pose errors and vertical drift, improving robot navigation accuracy in complex environments.
Area of Science:
- Robotics
- Sensor Fusion
- Simultaneous Localization and Mapping (SLAM)
Background:
- Quadruped robots offer terrain adaptability for tasks like inspection and rescue.
- Legged locomotion introduces vibrations and attitude changes challenging LiDAR odometry.
- Varying elevations and sparse vertical constraints degrade odometry accuracy, causing drift.
Purpose of the Study:
- To develop a robust LiDAR-only odometry framework for quadruped robots in elevation-varying environments.
- To address motion distortion and weak vertical observability issues in legged robot navigation.
- To improve the accuracy and reliability of pose estimation for quadruped robots.
Main Methods:
- Proposed a Piecewise-Constant Velocity deskewing scheme with safety clamping.
- Developed a two-stage Iterative Closest Point (ICP) algorithm decoupling SE(3) optimization.
- Applied observability-aware weighting in the vertical pose update stage.
Main Results:
- Achieved consistently lower Absolute Pose Error (APE) compared to ICP, KISS-ICP, and F-LOAM.
- Demonstrated significant suppression of vertical drift, especially in sequences with ramps.
- Validated the method on real-world data collected using a Unitree Go2 robot and Velodyne VLP-16 LiDAR.
Conclusions:
- The proposed odometry front-end enhances navigation accuracy for quadruped robots on challenging terrain.
- The novel deskewing and two-stage ICP methods effectively mitigate common LiDAR odometry failure modes.
- This work provides a more reliable solution for quadruped robot localization in complex, elevation-varying environments.
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