Robust Visual Place Recognition With Graph Kernels
Elena Stumm, Christopher Mei, Simon Lacroix, Juan Nieto, Marco Hutter, Roland Siegwart
Abstract
A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are based on graph kernel formulations, utilizing both the structural and visual information encoded in covisibility graphs. The proposed probabilistic model is able to circumvent the typically difficult and expensive posterior normalization procedure by exploiting the information available in visual observations. Furthermore, the place recognition complexity is independent of the size of the map. Results show improvements over the state-of-the-art on a diverse set of both public datasets and novel experiments, highlighting the benefit of the approach.
BibTeX
@inproceedings{cvpr2016_robustvisualplac,
title = {Robust Visual Place Recognition With Graph Kernels},
author = {Elena Stumm and Christopher Mei and Simon Lacroix and Juan Nieto and Marco Hutter and Roland Siegwart},
booktitle = {CVPR 2016},
year = {2016}
}