Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5616
Title: | Camera/LiDAR Sensor Fusion-based Autonomous Navigation | Authors: | Yusefi, A. Durdu, A. Toy, I. |
Keywords: | Autonomous Navigation Camera/LiDAR Sensor Fusion Deep Learning Obstacle Avoidance YOLOv7 Air navigation Collision avoidance Deep learning Ground vehicles Intelligent vehicle highway systems Navigation systems Object recognition Obstacle detectors Optical radar Autonomous navigation Camera/LiDAR sensor fusion Deep learning Distance estimation High rate Obstacles avoidance Obstacles detection Sensor fusion Sensor fusion systems YOLOv7 Cameras |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | This research presents a novel approach for autonomous navigation of Unmanned Ground Vehicles (UGV) using a camera and LiDAR sensor fusion system. The proposed method is designed to achieve a high rate of obstacle detection, distance estimation, and obstacle avoidance. In order to thoroughly study the form of things and decrease the problem of object occlusion, which frequently happens in camera-based object recognition, the 3D point cloud received from the LiDAR depth sensors is used. The proposed camera and LiDAR sensor fusion design balance the benefits and drawbacks of the two sensors to produce a detection system that is more reliable than others. The UGV's autonomous navigation system is then provided with the region proposal to re-plan its route and navigate appropriately. The experiments were conducted on a UGV system with high obstacle avoidance and fully autonomous navigation capabilities. The outcomes demonstrate that the suggested technique can successfully maneuver the UGV and detect impediments in actual situations. © 2024 IEEE. | Description: | Digitalni ozon Banja Luka;DWELT Software Banja Luka;et al.;MTEL Banja Luka;Municipality of East Ilidza;Municipality of East Stari Grad 23rd International Symposium INFOTEH-JAHORINA, INFOTEH 2024 -- 20 March 2024 through 22 March 2024 -- 199053 |
URI: | https://doi.org/10.1109/INFOTEH60418.2024.10495974 https://hdl.handle.net/20.500.13091/5616 |
ISBN: | 9798350329940 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Show full item record
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.