This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods.
@article{arxiv.1811.03032,
title = {A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes},
author = {Ahmad Khaliq and Shoaib Ehsan and Zetao Chen and Michael Milford and Klaus McDonald-Maier},
journal= {arXiv preprint arXiv:1811.03032},
year = {2019}
}
Comments
Conditionally Accepted as short paper at IEEE Transactions on Robotics (T-RO)