CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System
This work addresses trajectory estimation challenges in robotics and autonomous systems, offering an incremental improvement with a novel continuous-time framework for handling high-frequency, asynchronous sensor data.
The paper tackles the problem of accurate continuous-time trajectory estimation for LiDAR-inertial SLAM systems, particularly under aggressive motion, and shows that the proposed method outperforms discrete-time methods in accuracy on public and collected datasets.
In this paper, we propose a highly accurate continuous-time trajectory estimation framework dedicated to SLAM (Simultaneous Localization and Mapping) applications, which enables fuse high-frequency and asynchronous sensor data effectively. We apply the proposed framework in a 3D LiDAR-inertial system for evaluations. The proposed method adopts a non-rigid registration method for continuous-time trajectory estimation and simultaneously removing the motion distortion in LiDAR scans. Additionally, we propose a two-state continuous-time trajectory correction method to efficiently and efficiently tackle the computationally-intractable global optimization problem when loop closure happens. We examine the accuracy of the proposed approach on several publicly available datasets and the data we collected. The experimental results indicate that the proposed method outperforms the discrete-time methods regarding accuracy especially when aggressive motion occurs. Furthermore, we open source our code at \url{https://github.com/APRIL-ZJU/clins} to benefit research community.