TY - GEN
T1 - Point Cloud Map Generation and Localization for Autonomous Vehicles Using 3D Lidar Scans
AU - Poulose, Alwin
AU - Baek, Minjin
AU - Han, Dong Seog
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Autonomous vehicles are the future intelligent vehicles, which are expected to reduce the number of human drivers, improve efficiency, avoid collisions, and become the ideal city vehicles of the future. To achieve this goal, vehicle manufacturers have started to work in this field to harness the potential and solve current challenges to achieve the desired results. In this sense, the first challenge is transforming conventional vehicles into autonomous ones that meet users' expectations. The evolution of conventional vehicles into autonomous vehicles includes the adoption and improvement of different technologies and computer algorithms. The essential task affecting the autonomous vehicle's performance is its localization, apart from perception, path planning, and control, and the accuracy and efficiency of localization play a crucial role in autonomous driving. In this paper, we describe the implementation of map-based localization using point cloud matching for autonomous vehicles. The Robot Operating System (ROS) along with Autoware, which is an open-source software platform for autonomous vehicles, are utilized for the implementation of the vehicle localization system presented in this paper. Point cloud maps are generated based on 3D lidar points, and a normal distributions transform (NDT) matching algorithm is used for localizing the test vehicle through matching real-time lidar measurements with the pre-built point cloud maps. The experiment results show that the map-based localization system using 3D lidar scans enables real-time localization performance that is sufficiently accurate and efficient for autonomous driving in a campus environment. The paper comprises the methods used for point cloud map generation and vehicle localization as well as the step-by-step procedure for the implementation with a ROS-based system for the purpose of autonomous driving.
AB - Autonomous vehicles are the future intelligent vehicles, which are expected to reduce the number of human drivers, improve efficiency, avoid collisions, and become the ideal city vehicles of the future. To achieve this goal, vehicle manufacturers have started to work in this field to harness the potential and solve current challenges to achieve the desired results. In this sense, the first challenge is transforming conventional vehicles into autonomous ones that meet users' expectations. The evolution of conventional vehicles into autonomous vehicles includes the adoption and improvement of different technologies and computer algorithms. The essential task affecting the autonomous vehicle's performance is its localization, apart from perception, path planning, and control, and the accuracy and efficiency of localization play a crucial role in autonomous driving. In this paper, we describe the implementation of map-based localization using point cloud matching for autonomous vehicles. The Robot Operating System (ROS) along with Autoware, which is an open-source software platform for autonomous vehicles, are utilized for the implementation of the vehicle localization system presented in this paper. Point cloud maps are generated based on 3D lidar points, and a normal distributions transform (NDT) matching algorithm is used for localizing the test vehicle through matching real-time lidar measurements with the pre-built point cloud maps. The experiment results show that the map-based localization system using 3D lidar scans enables real-time localization performance that is sufficiently accurate and efficient for autonomous driving in a campus environment. The paper comprises the methods used for point cloud map generation and vehicle localization as well as the step-by-step procedure for the implementation with a ROS-based system for the purpose of autonomous driving.
KW - autonomous driving
KW - Autoware
KW - localization
KW - map generation
KW - map matching
KW - normal distributions transform (NDT)
KW - point cloud data
UR - http://www.scopus.com/inward/record.url?scp=85143059937&partnerID=8YFLogxK
U2 - 10.1109/APCC55198.2022.9943630
DO - 10.1109/APCC55198.2022.9943630
M3 - Conference contribution
AN - SCOPUS:85143059937
T3 - APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era
SP - 336
EP - 341
BT - APCC 2022 - 27th Asia-Pacific Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th Asia-Pacific Conference on Communications, APCC 2022
Y2 - 19 October 2022 through 21 October 2022
ER -