期刊
IEEE TRANSACTIONS ON ROBOTICS
卷 30, 期 5, 页码 1078-1090出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2014.2320795
关键词
Active object classification and pose estimation; hypothesis testing; motion control; planning and control for mobile sensors; recognition; robotics; vocabulary tree
类别
资金
- TerraSwarm (one of the six centers of STARnet, a Semiconductor Research Corporation program - MARCO)
- TerraSwarm (one of the six centers of STARnet, a Semiconductor Research Corporation program - DARPA)
- Grant ARL MAST-CTA [W911NF-08-2-0004]
- Grant ARL RCTA [W911NF-10-2-0016]
- [NSF-IIP-0742304]
- [NSF-OIA-1028009]
- [NSF-DGE-0966142]
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing, and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection in which the point of view of a mobile depth camera is controlled. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. Then, a sequence of views, which balances the amount of energy used to move the sensor with the chance of identifying the correct hypothesis, is planned. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate partially observable Markov decision process algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that the approach outperforms the widely used greedy viewpoint selection and provides a significant improvement over static object detection.
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