Authors:
Mehmet Ali Çağrı Tuncer
and
Dirk Schulz
Affiliation:
Fraunhofer FKIE, Germany
Keyword(s):
Object Segmentation, Distance Dependent Chinese Restaurant Process, Mean Shift, 3D Lidar Data.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
Abstract:
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 pos
sible 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.
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