  In the context of autonomous forklifts, ensuring non-collision during travel,
pick, and place operations is crucial. To accomplish this, the forklift must be
able to detect and locate areas of free space and potential obstacles in its
environment. However, this is particularly challenging in highly dynamic
environments, such as factory sites and production halls, due to numerous
industrial trucks and workers moving throughout the area. In this paper, we
present a novel method for free space detection, which consists of the
following steps. We introduce a novel technique for surface normal estimation
relying on spherical projected LiDAR data. Subsequently, we employ the
estimated surface normals to detect free space. The presented method is a
heuristic approach that does not require labeling and can ensure real-time
application due to high processing speed. The effectiveness of the proposed
method is demonstrated through its application to a real-world dataset obtained
on a factory site both indoors and outdoors, and its evaluation on the Semantic
KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of
50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In
addition, we evaluated our approach on our factory site dataset. Our method
achieved a mIoU score of 63.30 % at 54 Hz
