Positive and Negative Obstacles Detection Based on Dual-LiDAR in Field Environments

In the field of autonomous land vehicles (ALVs), environmental perception serves as the gateway for ALVs to acquire external information, directly determining the capabilities and intelligence level of autonomous driving. In the research of environmental perception technology for ALVs in field environments, achieving real-time, accurate, and robust obstacle detection is fundamental to realizing autonomous navigation. However, existing research often ignores the detection of negative obstacles, making the detection of positive and negative obstacles crucial for the safe navigation of ALVs. Therefore, we propose a method for ALV to use dual-LiDAR for positive and negative obstacles detection. The ground segmentation suffers from issues such as scene-specificity and threshold dependence. This method employs a horizontally mounted LiDAR to achieve ground segmentation and positive obstacle detection utilizing an adaptive threshold ground point cloud segmentation method with an improved ray method. Subsequently, a vertically mounted LiDAR is used to detect negative obstacles through local analysis of point clouds and clustering of feature point pairs to achieve local obstacle avoidance. A series of experiments were conducted in multiple field environments, and the results demonstrate that the proposed method achieves a successful detection rate of 98.5% for positive obstacles and 98.7% for negative obstacles in different field environments.