Fast detection of objects in a home or office environment is relevant for robotic service and assistance applications. In this work we present the automatic localization of a wide variety of differently shaped objects scanned with a laser range sensor from one view in a cluttered setting. The daily-life objects are modeled using approximated Superquadrics, which can be obtained from showing the object or another modeling process. Detection is based on a hierarchical RANSAC search to obtain fast detection results and the voting of sorted quality-offit criteria. The probabilistic search starts from low resolution and refines hypotheses at increasingly higher resolution levels. Criteria for object shape and the relationship of object parts together with a ranking procedure and a ranked voting process result in a combined ranking of hypothesis using a minimum number of parameters. Experiments from cluttered table top scenes demonstrate the effectiveness and robustness of the approach, feasible for real world object localization and robot grasp planning. © 2007 IEEE.
CITATION STYLE
Biegelbauer, G., & Vincze, M. (2007). Efficient 3D object detection by fitting superquadrics to range image data for robot’s object manipulation. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 1086–1091). https://doi.org/10.1109/ROBOT.2007.363129
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