Lin Zhihao, Tian Zhen, Zhang Qi, Zhuang Hanyang, Lan Jianglin
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Faculty of Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
Sensors (Basel). 2024 Sep 27;24(19):6258. doi: 10.3390/s24196258.
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, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a 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. The proposed 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.
本文提出了一种基于视觉的轻型自动驾驶汽车避障策略，该策略可以使用单个RGB-D相机在仅配备CPU的设备上运行。该方法包括两个步骤：视觉感知和路径规划。视觉感知部分使用通过光流增强的ORBSLAM3来估计汽车的位姿，并从场景中提取丰富的纹理信息。在路径规划阶段，该方法采用一种将控制李雅普诺夫函数和控制障碍函数以二次规划（CLF-CBF-QP）的形式相结合的方法，以及一个障碍物形状重建过程（SRP）来规划安全稳定的轨迹。为了验证所提方法的性能和鲁棒性，使用Gazebo仿真环境在各种复杂室内环境下对一辆汽车进行了仿真实验。所提方法能够有效避免场景中的障碍物。在所实现的多个模拟场景中，所提算法在获得更稳定、更短的轨迹方面优于基准算法。
