ATI-CTLO:Adaptive Temporal Interval-based Continuous-Time LiDAR-Only Odometry

TL;DR

This paper introduces an adaptive continuous-time LiDAR odometry method that dynamically adjusts control node intervals for improved accuracy and robustness in challenging environments with aggressive motion and sparse features.

Contribution

The proposed method adaptively adjusts temporal intervals between control nodes using linear interpolation, enhancing performance and robustness over existing LiDAR odometry solutions.

Findings

Achieves accuracy comparable to state-of-the-art methods.

Outperforms existing solutions in aggressive motion scenarios.

Improves robustness in feature-sparse environments.

Abstract

The motion distortion in LiDAR scans caused by aggressive robot motion and varying terrain features significantly impacts the positioning and mapping performance of 3D LiDAR odometry. Existing distortion correction solutions often struggle to balance computational complexity and accuracy. In this work, we propose an Adaptive Temporal Interval-based Continuous-Time LiDAR-only Odometry, utilizing straightforward and efficient linear interpolation. Our method flexibly adjusts the temporal intervals between control nodes according to the dynamics of motion and environmental characteristics. This adaptability enhances performance across various motion states and improves robustness in challenging, feature-sparse environments. We validate the effectiveness of our method on multiple datasets across different platforms, achieving accuracy comparable to state-of-the-art LiDAR-only odometry methods. Notably, in scenarios involving aggressive motion and sparse features, our method outperforms existing solutions.