Doppler-enhanced FMCW LiDAR odometry based on linear continuous-time representation

Abstract. LiDAR-based simultaneous localization and mapping (SLAM) plays a crucial role in applications such as search and rescue, infrastructure inspection, and underground exploration. However, conventional LiDAR-based methods often exhibit significantly reduced accuracy in degenerate environments. To address this challenge, this paper proposes a simple yet effective linear continuous-time FMCW (Frequency-Modulated Continuous Wave) LiDAR odometry method that tightly integrates Doppler constraints and point-to-plane constraints within a sliding-window-based factor graph optimization framework. The proposed method is comprehensively validated using datasets collected from a vehicle equipped with an Aeva I FMCW LiDAR in both typically degenerate scenes and highway scenarios. Experimental results demonstrate that the proposed method achieves the lowest trajectory root mean square error (RMSE) among the three sequences out of the total eight sequences, outperforming all compared methods. Notably, on Sequence 7, which spans an approximately trajectory length of 7,300 m, our method achieves a minimum trajectory RMSE of 10.19 m.