Memory Management for Real-Time Appearance-Based Loop Closure Detection
<|reference_start|>Memory Management for Real-Time Appearance-Based Loop Closure Detection: Loop closure detection is the process involved when trying to find a match between the current and a previously visited locations in SLAM. Over time, the amount of time required to process new observations increases with the size of the internal map, which may influence real-time processing. In this paper, we present a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on a memory management method that keeps computation time for each new observation under a fixed limit. Results demonstrate the approach's adaptability and scalability using four standard data sets.<|reference_end|>
@article{labbé2024memory,
    title={Memory Management for Real-Time Appearance-Based Loop Closure Detection},
    author={Mathieu Labb'e and Franc{c}ois Michaud},
    journal={IEEE/RSJ International Conference on Intelligent Robots and
  Systems, San Francisco, CA, USA, 2011, pp. 1271-1276},
    year={2024},
    doi={10.1109/IROS.2011.6094602},
    archivePrefix={arXiv},
    eprint={2407.15890},
    primaryClass={cs.RO cs.CV}
}