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A hybrid method using temporal and spatial information for 3D lidar data segmentation

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


Gusikhin, O. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
ICINCO 2017, 14th International Conference on Informatics in Control, Automation and Robotics. Proceedings. Vol.2 : Madrid, Spain, July 26-28, 2017
SciTePress, 2017
ISBN: 978-989-758-264-6
International Conference on Informatics in Control, Automation and Robotics (ICINCO) <14, 2017, Madrid>
Conference Paper
Fraunhofer FKIE ()

This paper proposes a novel hybrid segmentation method for 3D Light Detection and Ranging (Lidar) data. The presented approach gains robustness against the under-segmentation issue, i.e., assigning several objects to one segment, by jointly using spatial and temporal information to discriminate nearby objects in the data. When an autonomous vehicle has a complex dynamic environment, such as pedestrians walking close to their nearby objects, determining if a segment consists of one or multiple objects can be difficult with spatial features alone. The temporal cues allow us to resolve such ambiguities. In order to get temporal information, a motion field of the environment is estimated for subsequent 3D Lidar scans based on an occupancy grid representation. Then we propose a hybrid approach using the mean-shift method and the distance dependent Chinese Restaurant Process (ddCRP). After the segmentation blobs are spatially extracted from the scene, the mean-shift seeks the number of possible objects in the state space of each blob. If the mean-shift algorithm determines an under-segmented blob, the ddCRP performs the final partition in this blob. Otherwise, the queried blob remains the same and it is assigned as a segment. The computational time of the hybrid method is below the scanning period of the Lidar sensor. This enables the system to run in real time.