CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13064 > Article
Paper
20 February 2024 A 3D map construction method for autonomous vehicles in unknown environment
Peng Yu, Chunning Jin
Author Affiliations +
Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130643X (2024) https://doi.org/10.1117/12.3015775
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China
Abstract
Environment map construction is one of the key research directions of autonomous driving technology. Autonomous vehicle driving in unknown environments need to detect obstacles and identify passable areas. Traditional methods usually use two-dimensional planar maps without vertical information, and cannot exclude the influence of dynamic obstacles. In order to reflect the unknown environment information more accurately, this paper proposes a 3D map construction method based on Apollo autonomous driving system. Firstly, the point cloud data and pose data were obtained by the sensors mounted on the vehicle. Secondly, the 3D environment space was divided into different grids by the occupancy grid map algorithm. Then, the coordinates of different state grids are updated by ray casting algorithm. Finally, the Point Pillar algorithm is used to identify the dynamic obstacles and eliminate the residual shadows to obtain a more accurate 3D map. The experimental results show that the proposed method can effectively construct the unknown environment map, accurately reflect the three-dimensional information in the environment, and avoid the influence of dynamic obstacles.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Peng Yu and Chunning Jin "A 3D map construction method for autonomous vehicles in unknown environment", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130643X (20 February 2024); https://doi.org/10.1117/12.3015775
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Point clouds
Autonomous driving
Unmanned vehicles
Autonomous vehicles
Sensors
LIDAR
Object detection
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top