FMCW-LIO: A Doppler LiDAR-Inertial Odometry

2026-09-24

AI Summary

This study addresses the vulnerability of conventional LiDAR-inertial odometry (LIO) systems, which rely on geometric features and are prone to failure in structurally degenerate environments. We propose the first robust LIO framework that integrates velocity measurements from frequency-modulated continuous-wave (FMCW) Doppler LiDAR. By formulating motion compensation and observation models based on manifold state estimation, the proposed method leverages Doppler velocities to effectively reject dynamic outliers, thereby overcoming the inherent limitations of purely geometry-based approaches. Experimental evaluations demonstrate that our approach significantly outperforms existing algorithms in both localization accuracy and robustness across diverse challenging scenarios. This work establishes a novel paradigm for reliable perception in degenerate environments.

Abstract

Conventional LiDAR-inertial odometry (LIO) or simultaneous localization and mapping (SLAM) methods heavily rely on geometric features of environments, as LiDARs primarily provide range measurements instead of motion measurements. From now on, however, the situation changes thanks to the novel Frequency Modulated Continuous Wave (FMCW) Doppler LiDARs. FMCW Doppler LiDARs not only offer the point range with high resolution but also capture the instant point Doppler velocity through the Doppler effect. In the letter, we propose FMCW-LIO, a novel and robust LIO, leveraging intrinsic Doppler measurements from FMCW Doppler LiDARs. To correctly exploit Doppler velocities, a motion compensation method is designed, and a Doppler-aided observation model is applied for on-manifold state estimation. Then, dynamic points can be effectively removed by the Doppler criteria, deriving more consistent geometric observations. FMCW-LIO eventually achieves accurate state estimation and static mapping, even in structure-degenerated environments. Extensive experiments in diverse scenes are performed and FMCW-LIO outperforms other algorithms on both accuracy and robustness.

Problem

• LiDAR-inertial odometry
• FMCW Doppler LiDAR
• state estimation
• structure-degenerated environments
• dynamic point removal

Innovation

• FMCW Doppler LiDAR
• LiDAR-Inertial Odometry
• Motion Compensation
• On-manifold State Estimation
• Dynamic Point Removal