Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
By Mathieu Labbé, François Michaud. IEEE Transactions on robotics, 2013
377 citations recorded by the source corpus.
In appearance-based localization and mapping, loop-closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop-closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop-closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a working memory (WM) that is used for loop-closure detection, and transferring the others into a long-term memory (LTM). When a match is found between the current location and one stored in WM, associated locations that are stored in LTM can be updated and remembered for additional loop-closure detections. Results demonstrate the approach's adaptability and scalability using ten standard datasets from other appearance-based loop-closure approaches, one custom dataset using real images taken over a 2-km loop of our university campus, and one custom dataset (7 h) using virtual images from the racing video game “Need for Speed: Most Wanted”.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/046a7a57f2fc6db6f74fa937c37451385607baa0
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Citations in the corpus
- RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “Loop closure detection is done using the bag ‐ of ‐ words approach described in Labbé & Michaud (2013).”
- Online global loop closure detection for large-scale multi-session graph-based SLAM (2014, methodology): “Memory management approaches [16] can be used to limit the size of the map so that loop closure detections are always processed under a fixed time limit, thus satisfying online requirements for long-term and large-scale environment mapping.”