LiDAR-Based Negative Obstacle Detection for Unmanned Ground Vehicles in Orchards.

In orchard environments, negative obstacles such as ditches and potholes pose significant safety risks to robots working within them. This paper proposes a negative obstacle detection method based on LiDAR tilt mounting. With the LiDAR tilted at 40°, the blind spot is reduced from 3 m to 0.21 m, and the ground point cloud density is increased by an order of magnitude. Based on geometric features of laser point clouds (such as rear wall height and density, and spacing jump between points), a method for detecting negative obstacles is presented. This method establishes a mathematical model by analyzing changes in point cloud height, density, and point spacing, integrating features captured from multiple frames to enhance detection accuracy. Experiments demonstrate that this approach effectively detects negative obstacles in orchard environments, achieving a success rate of 92.7% in obstacle detection. The maximum detection distance reaches approximately 8.0 m, significantly mitigating threats posed to robots by negative obstacles in orchards. This research contributes valuable technological advancements for future orchard automation.