CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System

TL;DR

This paper introduces CLINS, a continuous-time trajectory estimation framework for LiDAR-inertial SLAM that improves accuracy during aggressive motion by effectively fusing asynchronous sensor data and removing motion distortion.

Contribution

The paper presents a novel continuous-time estimation framework with a non-rigid registration method and a two-state correction approach, advancing LiDAR-inertial SLAM accuracy and efficiency.

Findings

Outperforms discrete-time methods in accuracy during aggressive motion.

Effectively fuses high-frequency, asynchronous sensor data.

Removes motion distortion in LiDAR scans.

Abstract

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.