Research on Navigation Methods for Tomato Picking Robots Based on Lidar and Visual Odometry

In the environment of tomato greenhouses, picking robots often encounter issues such as cumulative errors during autonomous navigation, which can lead to loss of positioning. To address this problem, this paper proposes a method that combines visual odometry and 3D LiDAR to significantly improve mapping and localization accuracy. Firstly, visual odometry data is generated through VINS-Fusion and combined with the Cartographer algorithm to construct a 2D grid map. Subsequently, this paper improves the A* global path planning algorithm and integrates it with the DWA local path planning algorithm, ensuring efficient navigation and obstacle avoidance for the robot in indoor environments. Experimental results show that this method can meet the navigation requirements of the tomato picking robot.