Author:
Ulrike Thomas
Affiliation:
German Aerospace Center, Germany
Keyword(s):
Object Localization, Pose Estimation, Time-of-Flight Sensors, Ransac.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
In this paper a Random Sample Consensus (Ransac) based algorithm for object localization in time-of-flight depth images is presented. In contrast to many other approaches for pose estimation, the algorithm does not need an inertial guess of the object’s pose, despite it is able to find objects in real time. This is achieved by hashing suitable object features in a pre-processing step. The approach is model based and only needs point clouds of objects, which can either be provided by a CAD systems or acquired from prior taken measurements. The implemented approach is not a simple Ransac approach, because the algorithm makes use of a more progressive sampling strategy, hence the here presented algorithm is rather a Progressive Sampling Consensus (Prosac) approach. As a consequence, the number of necessary iterations is reduced. The implementation has been evaluated with a couple of exemplary scenarios as they occur in real robotic applications. On the one hand, industrial parts are pic
ked out of a bin and on the other hand every day objects are located on a table.
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