Visual place recognition for autonomous mobile robot navigation using LoFTR and MAGSAC++

Abstract

Autonomous mobile robots are defined as robotic entities capable of independent movement and intelligent decision-making, relying on their ability to perceive and analyze their surroundings, including objects in their environment. In Simultaneous Localization and Mapping (SLAM) systems, loop closure is often achieved through visual place recognition techniques, where the system compares the current visual input with previously observed scenes to identify matches. In computer vision applications, Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) are popular feature extraction algorithms used for such as key point detection, matching, and image registration tasks. The choice of inlier threshold should be based on the specific characteristics of the application and the nature of the images being processed. It often requires experimentation and tuning to find the optimal balance between robustness and accuracy. It Utilizes the pre-trained Local Feature Transformer (LoFTR) and MAGSAC++ estimator to address these drawbacks by employing the number of inliers to determine the similarity between two images for visual place recognition. Our experiment demonstrates that the number of inliers can determine the similarity of locations between two images. Scale variations and translation in location significantly influence the resulting number of inliers. Comparing images from the same location and from different locations yields varying numbers of inliers. The number of inliers significantly influences the similarity of locations. At the same location, the number of inliers is above 150, while at different locations, the number is below 150.