@inproceedings{7c7b6ebd1ec94a93ab4f7fd0e0a69deb,
title = "Feature Scan Context aided Lidar-IMU Simultaneously Localization and Mapping",
abstract = "Precise simultaneously localization and mapping is necessary to self-driving cars. In this paper, we present a SLAM system fusing with lidar and IMU data. Considering that pose initial value is a key problem for point cloud ICP alignment, we propose a method using the Extended Kalman Filter to combine initial yaw value obtained by feature scan context with the preintegrated IMU estimation value, aiming to improve the initial yaw value of the vehicle. In addition, we adopt the feature scan context to the loop closure, which is beneficial to the whole SLAM system to reduce the accumulative errors. Sufficient experiments are carried out in outdoor environment. The results show that our method acquires significant superior performance comparing with other two main current lidar SLAM systems-LIO-SAM and LeGo-Loam.",
keywords = "SLAM, extended kalman filter, loop closure, multi-sensor fusion, scan context",
author = "Yan Wen and Lijin Han and Ying Li and Sihao Lin and Shida Nie and Xiaohui Jiang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023 ; Conference date: 27-10-2023 Through 29-10-2023",
year = "2023",
doi = "10.1109/CVCI59596.2023.10397389",
language = "English",
series = "Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023",
address = "United States",
}