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A framework for the segmentation and classification of 3D point clouds using temporal, spatial and semantic information

: Tuncer, M.A.C.; Schulz, D.


Madani, K. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal; International Federation of Automatic Control -IFAC-; IEEE Robotics and Automation Society:
15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018. Proceedings. Vol.2 : July 29-31, 2018, Porto, Portugal
SciTePress, 2018
ISBN: 978-989-758-321-6
International Conference on Informatics in Control, Automation and Robotics (ICINCO) <15, 2018, Porto>
Conference Paper
Fraunhofer FKIE ()

This paper proposes a novel framework for the segmentation and classification of 3D point cloud which jointly uses spatial, temporal and semantic information. It improves the classification performance by reducing under-segmentation errors. The presented framework, which can determine the number and label of objects in each spatially extracted blob, is decomposed into three steps to acquire spatial, temporal and semantic cues. For the spatial features, blobs are extracted spatially with a neighborhood system on an occupancy grid representation. A smoothed motion field is estimated for the acquisition of temporal cue, where the grid cells are tracked using individual Kalman filters and estimated velocities are transformed to one dimensional movement directions. A support vector machine (SVM) classifier is trained to discriminate the classes of interest for the semantic information of the blobs. A confidence metric is defined to probabilistically compare the volume of each classified bl ob with the volume of an average object for that class. If this metric is below a predefined threshold, a sequential variant of distance dependent Chinese restaurant process (s-ddCRP) performs the final partition in this blob by using spatial and temporal information. If the s-ddCRP approach splits the blob, the partitioned sub-blobs are afterwards reassigned to new objects by the classifier. Otherwise, the queried blob remains the same. This procedure iteratively continues while searching each blob in the scene at each time frame. Experiments on data obtained with a Velodyne HDL64 scanner in real traffic scenarios illustrate that the proposed framework improves the classification performance of an SVM classifier by reducing under-segmentation errors.