Static and Dynamic Objects Analysis as a 3D Vector Field
Static and Dynamic Objects Analysis as a 3D Vector Field
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DOI：
10.1109/3dv.2017.00035
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Cansen Jiang;D. Paudel;Y. Fougerolle;D. Fofi;C. Demonceaux
中科院分区：
文献类型：
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作者：
Cansen Jiang;D. Paudel;Y. Fougerolle;D. Fofi;C. Demonceaux
在场景建模、理解和基于地标的机器人导航的背景下，静态场景部件和运动物体及其运动行为的知识起着至关重要的作用。我们提出了一个完整的框架来检测和提取运动目标，以重建高质量的静态地图。对于移动的三维摄像机设置，我们提出了一种新的三维流场分析方法，该方法仅使用三维点云信息就能准确地检测移动物体。此外，我们还引入了稀疏流聚类方法来对运动流向量进行有效和鲁棒的分组。实验表明，本文提出的流场分析算法和稀疏流聚类方法在运动检测和分割方面是非常有效的，可以产生高质量的重建静态图和真实场景的刚性运动物体。
In the context of scene modelling, understanding, and landmark-based robot navigation, the knowledge of static scene parts and moving objects with their motion behaviours plays a vital role. We present a complete framework to detect and extract the moving objects to reconstruct a high quality static map. For a moving 3D camera setup, we propose a novel 3D Flow Field Analysis approach which accurately detects the moving objects using only 3D point cloud information. Further, we introduce a Sparse Flow Clustering approach to effectively and robustly group the motion flow vectors. Experiments show that the proposed Flow Field Analysis algorithm and Sparse Flow Clustering approach are highly effective for motion detection and segmentation, and yield high quality reconstructed static maps as well as rigidly moving objects of real-world scenarios.