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Incremental Data Association for Acoustic Structure from Motion

Tiffany Huang and Michael Kaess
Conference Paper, Proceedings of IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, October, 2016

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We provide a novel incremental data association method to complement our previous work on acoustic structure from motion (ASFM), which recovers 3D scene structure from multiple 2D sonar images, while at the same time localizing the sonar. Given point features extracted from multiple overlapping sonar images, our algorithm automatically finds the correspondences between the features. Our data association method uses information about the geometric correlations of the entire set of landmarks to reject spurious measurements or false positives that might otherwise have been accepted. For each new sonar measurement, the algorithm uses a gating procedure to narrow the landmark match search space. Using the pruned surviving candidate correspondences, we identify the correct hypothesis based on a posterior compatibility cost, penalizing for null matches to avoid all measurements being declared new landmarks. Unlike other methods, ASFM does not require any planar scene assumptions and uses constraints from more than two images to increase accuracy in both mapping and localization. We evaluate our algorithm in simulation and demonstrate successful data association results on real sonar images.

author = {Tiffany Huang and Michael Kaess},
title = {Incremental Data Association for Acoustic Structure from Motion},
booktitle = {Proceedings of IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
year = {2016},
month = {October},
} 2017-09-13T10:38:13-04:00