# Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation

arXiv: https://arxiv.org/abs/2407.15304 · HTML: https://mlsanity.com/paper/2407.15304/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 54 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [MarsLGPR: Mars Rover Localization with Ground Penetrating Radar](https://mlsanity.com/paper/2503.04944/) (2025)
- [PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection](https://mlsanity.com/paper/2504.08280/) (2025)
- [LoopNet: A Multitasking Few-Shot Learning Approach for Loop Closure in Large Scale SLAM](https://mlsanity.com/paper/2507.15109/) (2025)
- [Bag-of-Word-Groups (BoWG): A Robust and Efficient Loop Closure Detection Method Under Perceptual Aliasing](https://mlsanity.com/paper/2510.22529/) (2025)
- [EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition](https://mlsanity.com/paper/2602.12919/) (2026)
- [DTIF: Robust Loop Closure Detection via Delaunay Triangle Topology in Complex Forests](https://mlsanity.com/paper/2607.21138/) (2026)

## Abstract (arXiv)

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) 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 stored in LTM can be updated and remembered for additional loop closure detections. Results demonstrate the approach's adaptability and scalability using ten standard data sets from other appearance-based loop closure approaches, one custom data set using real images taken over a 2 km loop of our university campus, and one custom data set (7 hours) using virtual images from the racing video game ``Need for Speed: Most Wanted''.