A&B-LO: Continuous-Time LiDAR Odometry with Adaptive Non-Uniform B-Spline Trajectory Representation
Yuchu Lu, Chenpeng Yao, Jiayuan Du, Chengju Liu, Qijun Chen
AI summary
Problem
Discrete-time LiDAR odometry fails under aggressive motion due to constant-velocity deskewing assumptions, while LiDAR-inertial systems suffer from IMU saturation and inconsistency. The paper addresses how to accurately compensate motion distortion in real-time without relying on error-prone inertial sensors.
Approach
The method formulates odometry as a continuous-time estimation problem using a non-uniform B-spline trajectory model. It combines point-to-plane registration with pseudo-velocity smoothing constraints, derives analytical Jacobians for fast optimization, and dynamically adjusts control point spacing based on motion complexity.
Key results
- Compact LiDAR-only pipeline eliminating IMU dependency
- Analytical Jacobian derivation accelerating optimization
- Adaptive knot spacing balancing efficiency and accuracy
- Competitive accuracy and real-time performance on public datasets
Why it matters
Enables robust, high-frequency navigation for autonomous robots in aggressive or IMU-saturated environments, advancing real-time SLAM for resource-constrained systems.
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 mini- mization. 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.