Wavemap achieves state-of-the-art memory and computational efficiency by combining Haar wavelet compression and a coarse-to-fine measurement integration scheme. Advanced measurement models allow it to attain exceptionally high recall rates on challenging obstacles like thin objects.
The framework is very flexible and supports several data structures, measurement integration methods, and sensor models out of the box. The ROS interface can, for example, easily be configured to fuse multiple sensor inputs, such as a LiDAR configured with a range of 20m and several depth cameras up to a resolution of 1cm, into a single map.
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The framework's documentation is hosted on GitHub Pages.
A technical introduction to the theory behind wavemap is provided in our open-access RSS paper, available here. For a quick overview, watch the accompanying 5-minute presentation here.
Please cite this paper when using wavemap for research.
APA-style:
Reijgwart, V., Cadena, C., Siegwart, R., & Ott, L. (2023). Efficient volumetric mapping of multi-scale environments using wavelet-based compression. Proceedings of Robotics: Science and Systems XIX. https://doi.org/10.15607/RSS.2023.XIX.065
BibTeX:
@INPROCEEDINGS{reijgwart2023wavemap,
author = {Reijgwart, Victor and Cadena, Cesar and Siegwart, Roland and Ott, Lionel},
journal = {Robotics: Science and Systems. Online Proceedings},
title = {Efficient volumetric mapping of multi-scale environments using wavelet-based compression},
year = {2023-07},
}
Note that the code has significantly improved since the paper was written. Wavemap is now up to 10x faster, thanks to new multi-threaded measurement integrators, and uses up to 50% less RAM, by virtue of new memory efficient data structures inspired by OpenVDB.