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Omnidirectional Visual Place Recognition using Rotation Invariant Sequence Matching

Peter Hansen and Brett Browning
Tech. Report, CMU-RI-TR-15-103, CMU-CS-QTR-126, Robotics Institute, Carnegie Mellon University, March, 2015

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In this work we develop a visual place recognition system for omni-directional panoramic images that makes use of their full 360 degree circumferential angle of view. The system builds on our previous variant of the sequence SLAM algorithm to include panoramic image rotation alignment for improved image similarity scoring, and bi-directional query and database sequence matching within a Hidden Markov Model (HMM) framework for robust place recognition. Three rotation alignment methods are explored including image pixel registration, and both image cross correlation and zero phase alignment in the spherical Fourier domain. All alignment methods operate using low-resolution images for computational efficiency. Experiments using an outdoor panoramic image dataset demonstrate improved precision recall performance using rotation alignment and bi-directional sequence matching. In particular, place recognition is possible in scenarios where a robot traverses a previous path in the opposite direction.

This publication was made possible by YSREP grant #1-019-2-008 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

author = {Peter Hansen and Brett Browning},
title = {Omnidirectional Visual Place Recognition using Rotation Invariant Sequence Matching},
year = {2015},
month = {March},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-15-103, CMU-CS-QTR-126},
} 2017-09-13T10:38:46-04:00