Memory Management for Real-Time Appearance-Based Loop Closure Detection

• URL: http://arxiv.org/abs/2407.15890v1
• Date: Mon, 22 Jul 2024 00:24:12 GMT
• Title: Memory Management for Real-Time Appearance-Based Loop Closure Detection
• Authors: Mathieu Labbé, François Michaud,
• Abstract summary: 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 time for each new observation under a fixed limit.
• Score: 1.1279808969568252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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