  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.
