Chanseok Park’s research while affiliated with Korea Institute of Machinery and Materials and other places

The paper presents a novel method for real-time detection of off-road drivable regions in LiDAR point clouds without relying on deep learning. Our method directly distinguishes drivable regions while accounting for noise caused by snow and fog. By focusing on the scanning characteristics of rotating LiDAR, our PIIC approach achieves robust performance in challenging off-road environments. The proposed technique divides the space into sections and segments drivable areas by counting points in each section. It allows noise influence to be countable using a simple probabilistic model. The developed solution offers significant advancements in autonomous driving in off-road environments since the method offers drivability in real-time with noise immunity. Experimental results demonstrate the efficiency of the proposed method in hazardous situations such as heavy snow, fog, and crosstalk from other LiDAR signals. Importantly, the low computational load enables real-time operation and provides promising opportunities for fusion with other recognition methods.