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.

翻译：由机器人剧烈运动及多变地形特征引起的激光雷达扫描运动畸变，显著影响了三维激光雷达里程计的定位与建图性能。现有畸变校正方案往往难以平衡计算复杂度与精度。本研究提出一种基于自适应时间间隔的连续时间纯激光雷达里程计方法，采用简洁高效的线性插值技术。该方法依据运动动态与环境特征，灵活调整控制节点间的时间间隔。这种自适应性提升了不同运动状态下的性能表现，并增强了在具有挑战性的特征稀疏环境中的鲁棒性。我们在多个不同平台的公开数据集上验证了本方法的有效性，其精度达到了当前先进纯激光雷达里程计方法的水平。值得注意的是，在剧烈运动与特征稀疏的场景中，本方法的表现优于现有解决方案。