%0 Conference Paper
%1 schreck2023height
%A Schreck, Steven
%A Reichert, Hannes
%A Hetzel, Manuel
%A Doll, Konrad
%A Sick, Bernhard
%B International Conference on Control, Mechatronics and Automation (ICCMA)
%D 2023
%I IEEE
%K imported itegpub isac-www
%P 171--176
%T Height Change Feature Based Free Space Detection
%X We present a novel method for free space detection in dynamic environments like factory sites, crucial for autonomous forklifts to avoid collisions. It introduces a technique for fast surface normal estimation using spherical projected LiDAR data, which helps in detecting free space efficiently and in real-time. The method's effectiveness is proven with a 50.90% mIoU score on the Semantic KITTI dataset at 105 Hz, and a 63.30% mIoU score on a factory site dataset at 54 Hz.