Reducing Communication Consumption in Collaborative Visual SLAM with Map Point Selection and Efficient Data Compression
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7590-7_2
Reference12 articles.
1. Park, Y., Bae, S.: Keeping less is more: point sparsification for visual SLAM. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, pp. 7936–7943 (2022)
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3. Van Opdenbosch, D., Aykut, T., Alt, N., Steinbach, E.: Efficient map compression for collaborative visual SLAM. In: 2018 IEEE Winter Conference on Applications of Computer Vision, WACV, pp. 992–1000 (2018)
4. Baroffio, L., Canclini, A., Cesana, M., Redondi, A., Tagliasacchi, M., Tubaro, S.: Coding local and global binary visual features extracted from video sequences. IEEE Trans. Image Process. 24(11), 3546–3560 (2015)
5. Schmuck, P., Chli, M.: CCM-SLAM: robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams. J. Field Robot. 36(4), 763–781 (2019)
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