A&B-LO: Continuous-Time LiDAR Odometry with Adaptive Non-Uniform B-Spline Trajectory Representation

Abstract

LiDAR odometry, fused by inertial measurement units (IMU), is an essential task in robotics navigation. Unlike the mainstream methods compensate the motion distortion of LiDAR data by high frequency inertial sensors, this paper deals with the distortion with continuous-time trajectory representation, and achieved competitive performance against state-of-the-art. We propose a compact framework of LiDAR odometry with adaptive non-uniform B-spline trajectory representation to formulate it as continuous-time estimation problem. We deploy point-to-plane registration and pseudo-velocity smoothing constraints to fully utilize geometric and kinematic information of odometry. For faster convergence of optimization, analytical Jacobian of constraints is derived to solve the non-linear least squares minimization. For more efficient B-spline representation, an adaptive knot spacing technique is proposed to adjust the time interval of control poses of spline. Extensive experiments on public and realistic datasets demonstrate validation and efficiency of our system compared with other LiDAR or LiDAR-inertial methods.