Continuous plane detection in point-cloud data based on 3D Hough Transform
| Autor: | Zdenek Materna, Michal Spanel, Rostislav Hulik, Pavel Smrz |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Computational complexity theory
Computer science Plane (geometry) business.industry Detector 0211 other engineering and technologies Point cloud Context (language use) 02 engineering and technology RANSAC Hough transform law.invention law Signal Processing 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Point (geometry) Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business 021101 geological & geomatics engineering |
| Zdroj: | Journal of Visual Communication and Image Representation |
| ISSN: | 1047-3203 |
| DOI: | 10.1016/j.jvcir.2013.04.001 |
| Popis: | We propose a 3D Hough Transform plane detector for depth sensors.Several significant optimizations are proposed to maximize its practical usability.Continuous flow of frames is used to accumulate and iteratively refine detected planes.Comparison with another widely used plane extraction, RANSAC, is provided. This paper deals with shape extraction from depth images (point clouds) in the context of modern robotic vision systems. It presents various optimizations of the 3D Hough Transform used for plane extraction from point cloud data. Presented enhancements of standard methods address problems related to noisy data, high memory requirements for the parameter space and computational complexity of point accumulations. The realised robust plane detector benefits from a continuous point cloud stream generated by a depth sensor over time. It is used for iterative refinements of the results. The system is compared to a state-of-the-art RANSAC-based plane detector from the Point Cloud Library (PCL). Experimental results show that it overcomes the PCL alternative in the stability of plane detection and in the number of negative detections. This advantage is crucial for robotic applications, e.g., when a robot approaches a wall, it can be consistently recognized. The paper concludes with a discussion of further promising optimisation that will be implemented as a future step. |
| Databáze: | OpenAIRE |
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