6533b82bfe1ef96bd128d8d0

RESEARCH PRODUCT

Static and Dynamic Objects Analysis as a 3D Vector Field

Danda Pani PaudelCansen JiangYohan FougerolleCédric DemonceauxDavid Fofi

subject

0209 industrial biotechnologyComputer sciencebusiness.industry[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPoint cloud[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Context (language use)Motion detection02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020901 industrial engineering & automationFlow (mathematics)Motion estimation0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]020201 artificial intelligence & image processingSegmentationComputer visionArtificial intelligenceCluster analysisbusinessEuclidean vector

description

International audience; 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 seg-mentation, and yield high quality reconstructed static maps as well as rigidly moving objects of real-world scenarios.

https://doi.org/10.1109/3dv.2017.00035