3D OBJECT DETECTION BY FEATURE AGGREGATION USING POINT CLOUD INFORMATION FOR FACTORY OF THE FUTURE
Nowadays, object detection is considered as an unavoidable aspect that needs to be addressed in any robotic application, especially in industrial settings where robots and vehicles interact closely with humans and objects and therefore a high level of safety for workers and machines is required. This paper proposes an object detection framework suitable for automated vehicles in the factory of the future. It utilizes only point cloud information captured by LiDAR sensors. The system divides the point cloud into voxels and learns features from the calculated local patches. The aggregated feature samples are then used to iteratively train a classifier to recognize object classes. The framework is evaluated using a new synthetic 3D LiDAR dataset of objects that simulates large indoor point cloud scans of a factory model. It is also compared with other methods by evaluating on SUN RGB-D benchmark dataset. The evaluations reveal that the framework can achieve promising object recognition and detection results that we report as a baseline.