Localization from semantic observations via the matrix permanent
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are anno...
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Veröffentlicht in: | The International journal of robotics research 2016-01, Vol.35 (1-3), p.73-99 |
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Sprache: | eng |
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Zusammenfassung: | Most approaches to robot localization rely on low-level geometric features such as
points, lines, and planes. In this paper, we use object recognition to obtain semantic
information from the robot’s sensors and consider the task of localizing the robot within
a prior map of landmarks, which are annotated with semantic labels. As object recognition
algorithms miss detections and produce false alarms, correct data association between the
detections and the landmarks on the map is central to the semantic localization problem.
Instead of the traditional vector-based representation, we propose a sensor model, which
encodes the semantic observations via random finite sets and enables a unified treatment
of missed detections, false alarms, and data association. Our second contribution is to
reduce the problem of computing the likelihood of a set-valued observation to the problem
of computing a matrix permanent. It is this crucial transformation that allows us to solve
the semantic localization problem with a polynomial-time approximation to the set-based
Bayes filter. Finally, we address the active semantic localization problem, in which the
observer’s trajectory is planned in order to improve the accuracy and efficiency of the
localization process. The performance of our approach is demonstrated in simulation and in
real environments using deformable-part-model-based object detectors. Robust global
localization from semantic observations is demonstrated for a mobile robot, for the
Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with
the traditional lidar-based geometric Monte Carlo localization. |
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ISSN: | 0278-3649 1741-3176 |
DOI: | 10.1177/0278364915596589 |