Segmentation and matching: Towards a robust object detection system

Abstract

This paper focuses on detecting parts in laser-scanned data of a cluttered industrial scene. To achieve the goal, we propose a robust object detection system based on segmentation and matching, as well as an adaptive segmentation algorithm and an efficient pose extraction algorithm based on correspondence filtering. We also propose an overlapping-based criterion that exploits more information of the original point cloud than the number-of-matching criterion that only considers key-points. Experiments show how each component works and the results demonstrate the performance of our system compared to the state of the art.