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

TL;DR

This paper introduces an online appearance-based loop closure detection method that manages memory efficiently to enable scalable, real-time large-scale and long-term localization in robotics and mapping.

Contribution

It proposes a memory management strategy that maintains recent and frequent locations for loop closure detection, enhancing scalability and real-time performance.

Findings

Effective in large-scale environments

Scalable to long-term operation

Validated on diverse datasets

Abstract

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''.