Autonomous driving paper index
Ground Segmentation Method Based on Polar Grid for 3D LiDAR Point Cloud
One-line summary
In this paper, we propose a realtime, CPU-based ground segmentation method that structures the point cloud into a polar grid and estimates the ground points based on the local features of the grid cells.
Engineering notes
Our ground segmentation method is evaluated on the SemanticKITTI dataset and achieves high accuracy with real-time performance.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In autonomous driving platforms, one of the key tasks is the perception of the environment. Light Detection and Ranging (LiDAR) sensors are widely used to obtain a point cloud representation of the surrounding environment. Raw point clouds contain measurement points from different objects in the environment, and distinguishing and semantically segmenting them can be challenging, but beneficial for better understanding the environment. One of the challenges of preprocessing raw point clouds is their segmentation into groups based on the type of object they originated from. In this paper, we propose a realtime, CPU-based ground segmentation method that structures the point cloud into a polar grid and estimates the ground points based on the local features of the grid cells. Our ground segmentation method is evaluated on the SemanticKITTI dataset and achieves high accuracy with real-time performance.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments