CT-FLO: Simple Yet Effective FMCW LiDAR Odometry Using an Linear Continuous-Time Trajectory

Abstract

Frequency-Modulated Continuous-Wave (FMCW) LiDAR is capable of acquiring dense point clouds along with additional Doppler measurements. For discrete-time-based FMCW LiDAR odometry methods, motion distortion correction for both 3D and Doppler measurements should be performed before scan matching. However, this correction is inherently less accurate than continuous-time methods, and the residual errors are challenging to model. In this paper, we introduce CT-FLO, the first FMCW LiDAR odometry method that employs a linear continuous-time trajectory representation. Our method employs a sliding-window-based factor graph that incorporates point-to-plane factors, Doppler velocity factors, and a marginalization factor. We evaluate the proposed method on real-world datasets and demonstrate that it achieves excellent performance compared to state-of-the-art methods. The average root mean square of translational RPE, calculated using 100-meter sub-trajectories, was 1.7 m on geometrically challenging sequences and 0.19 m on feature-rich urban sequences. Additionally, an ablation study was conducted to investigate the contribution of the Doppler factor. We found that in a feature-rich environment with a longer time interval (e.g., 100 ms) for the control points, the Doppler factor can have a negative impact on the overall accuracy. Furthermore, we performed comparative experiments that determined the optimal time interval on a vehicle platform to be 40 ms for the test dataset. These results indicate that the optimal time interval is closely correlated with the geometric structure of the environment and the vehicle's motion state.