CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13176 > Article
Paper
22 May 2024 Obstacle detection method based on point cloud registration and target reduction processing efficient detection of vehicle obstacles
Dawei Pi, Lei Guan, Hongliang Wang
Author Affiliations +
Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762G (2024) https://doi.org/10.1117/12.3029266
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China
Abstract
Obstacle detection systems serve as a linchpin in ensuring the safety of autonomous vehicles. While contemporary detection techniques grapple with intricate results, diminished efficiency, and substantial computational burdens, our research introduces a groundbreaking method anchored in point cloud registration and target reduction for areas with obstructions. This approach not only facilitates high-precision positioning but also expedites the process of obstacle detection, a cornerstone for the subsequent navigation and obstacle evasion tasks in intelligent vehicles. Initially, we harness the power of NDT registration to achieve meticulous positioning. Subsequent stages involve refining the detection landscape by filtering out non-road obstacles using our innovative target reduction process and then employing advanced clustering techniques to pinpoint obstacles. We've validated our approach using the comprehensive Mulran dataset. Both qualitative and quantitative assessments underscore the method's robustness, reliability, and superior effectiveness. Experimental outcomes compellingly demonstrate that our newly proposed obstacle detection framework considerably elevates both operational efficiency and accuracy, paving the way for safer and smarter autonomous driving.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Dawei Pi, Lei Guan, and Hongliang Wang "Obstacle detection method based on point cloud registration and target reduction processing efficient detection of vehicle obstacles", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762G (22 May 2024); https://doi.org/10.1117/12.3029266
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
Roads
Target detection
Nondestructive evaluation
Tunable filters
LIDAR
Autonomous vehicles
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top