Dimensionality reduction through visual data resampling for low-storage loop-closure detection

As loop-closure detection plays a fundamental role in any simultaneous localization and mapping (SLAM) system, through its ability to recognize previously visited locations, one of its main objectives is to permit consistent map generation for an extended period. Within large-scale SLAM autonomy, the scalability in terms of timing needed for database search and the storage requirements has to be addressed. In this paper, a low-storage visual loop-closure detection technique is proposed. Our system is based on the incremental bag-of-tracked-words scheme for the trajectory mapping still, the generated visual representations are reduced to lower dimensions through a resampling process. This way, we achieve to shorten the overall database size and searching time, while at the same time preserving the high performance. The evaluation, which took place on different well-known datasets, exhibits the system’s low-storage requirements and high recall scores compared to the baseline version and other state-of-the-art approaches.

Konstantinos A. Tsintotas, Shan An, Ioannis Tsampikos Papapetros, Fotios K. Konstantinidis, Georgios Ch. Sirakoulis, Antonios Gasteratos

2022 IEEE International Conference on Imaging Systems and Techniques
Availability Date
July 20th, 2022
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