67 / 2021-07-19 13:20:41

An Outlier-Robust GNSS-inertial-LiDAR Odometry

Special Session > SS8 - Machine Learning for Industrial Internet of Things (IIoT)

We tackle a modified Outlier-Robust GNSS-inertial-LiDAR unmanned vehicle localization system based on the factor graph. The frame of the localization system utilizes graph optimization to fuse information from IMU pre-integration, GNSS and LiDAR-inertial odometry. In order to cope with the problem of high-precision localization in complex scenes in driving process of the unmanned vehicle, residual  outlier test added before graph optimization applies in this frame to effectively eliminate outliers from GNSS and LiDAR-inertial odometry and mitigate their influence to maintain robust localization. In addition, a fixed-time sliding window is organized in optimization to lower the computation, satisfying real-time requirements. Through extensive experiments in simulations, the results show that this system can provide a reliable localization result and takes advantage over Kalman filter and pure LiDAR algorithm.