Détail du document
Identifiant
oai:arXiv.org:2408.11582
Sujet
Computer Science - Robotics Electrical Engineering and Systems...Auteur
Lin, Zhihao Tian, Zhen Zhang, Qi Zhuang, Hanyang Lan, JianglinCatégorie
Computer Science
Année
2024
Date de référencement
28/08/2024
Mots clés
visual methodRésumé
The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera.
The method consists of two steps: visual perception and path planning.
The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car's poses and extract rich texture information from the scene.
In the path planning phase, we employ a method combining a control Lyapunov function and control barrier function in the form of quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories.
To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment.
Our method can effectively avoid obstacles in the scenes.
The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes.
;Comment: 16 pages; Submitted to a journal
Lin, Zhihao,Tian, Zhen,Zhang, Qi,Zhuang, Hanyang,Lan, Jianglin, 2024, Enhanced Visual SLAM for Collision-free Driving with Lightweight Autonomous Cars
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