An Optimization-Based Multi-Sensor Fusion Approach Towards Global Drift-Free Motion Estimation

Abstract

Precise and drift-free motion estimation is an essential technology for autonomous driving. Single-sensor methods such as laser-based or vision-based have proven to be inadequate. To solve the problem, we proposed an optimization-based fusion approach that incorporates information from complementary sensors to achieve high accuracy and global drift-free. The core idea is to construct a globally unified pose graph through a dual-layer optimization strategy. The local estimation layer obtains the relative pose through LiDAR odometry and visual-inertial odometry. Subsequently, by introducing the absolute geographic position information of GPS, the accumulated drifts are corrected in the global optimization layer. The performance of our approach has been evaluated both in real-world environments and public datasets. The result demonstrates that our approach outperforms other state-of-the-art algorithms, with an average translation error of 0.8045% and an average rotation error of 0.0043deg/m.