@inproceedings{93561c65d5a0471fa947ce0d4ccbc55a,
title = "Integrating V-SLAM and LiDAR-based SLAM for Map Updating",
abstract = "Vehicle positioning generally uses the global navigation satellite system (GNSS), but systems of different levels significantly affect positioning accuracy. Moreover, it is greatly affected by weather that may cause inaccurate positioning due to excessive cloud cover. In the technology of self-driving cars, how to achieve stable and accurate positioning is a critical topic. This research first uses RTK-GPS, LiDAR, and cameras to build high-precision map information and realize vehicle positioning functions. By using the normal distribution transform (NDT), ORB-SLAM, and iterative closest point (ICP) were matched to the feature points of the current map with the pre-built map to complete the vehicle positioning system. If the change of static objects in the map was detected during the positioning process, the map was updated. Finally, the performance of the proposed system was verified on the road on the campus.",
keywords = "HD Map, LiDAR SLAM, Point Cloud Map Updating, Visual SLAM",
author = "Chang, {Yu Cheng} and Chen, {Ya Li} and Hsu, {Ya Wen} and Perng, {Jau Woei} and Chang, {Jun Dong}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2021 ; Conference date: 23-07-2021 Through 25-07-2021",
year = "2021",
month = jul,
day = "23",
doi = "10.1109/ICKII51822.2021.9574718",
language = "English",
series = "4th IEEE International Conference on Knowledge Innovation and Invention 2021, ICKII 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "134--139",
editor = "Teen-Hang Meen",
booktitle = "4th IEEE International Conference on Knowledge Innovation and Invention 2021, ICKII 2021",
address = "美國",
}