Lightweight, viewpoint-invariant visual place recognition in changing environments
S Lowry, H Andreasson - IEEE Robotics and Automation …, 2018 - ieeexplore.ieee.org
IEEE Robotics and Automation Letters, 2018•ieeexplore.ieee.org
This letter presents a viewpoint-invariant place recognition algorithm which is robust to
changing environments while requiring only a small memory footprint. It demonstrates that
condition-invariant local features can be combined with Vectors of Locally Aggregated
Descriptors to reduce high-dimensional representations of images to compact binary
signatures while retaining place matching capability across visually dissimilar conditions.
This system provides a speed-up of two orders of magnitude over direct feature matching …
changing environments while requiring only a small memory footprint. It demonstrates that
condition-invariant local features can be combined with Vectors of Locally Aggregated
Descriptors to reduce high-dimensional representations of images to compact binary
signatures while retaining place matching capability across visually dissimilar conditions.
This system provides a speed-up of two orders of magnitude over direct feature matching …
This letter presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from nonaligned images can be achieved in visually changing environments with as few as 256 b (32 B) per image.
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