Height Change Feature Based Free Space Detection

TL;DR

This paper introduces a real-time free space detection method for autonomous forklifts using surface normal estimation from spherical LiDAR data, achieving high accuracy and speed in dynamic industrial environments.

Contribution

The paper presents a novel, label-free surface normal estimation technique from spherical LiDAR data for free space detection in industrial settings.

Findings

Achieved 50.90% mIoU on Semantic KITTI dataset

Processed data at 105 Hz for real-time application

Attained 63.30% mIoU on factory site dataset at 54 Hz

Abstract

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