Adaptive Thresholding for Visual Place Recognition using Negative Gaussian Mixture Statistics

TL;DR

The paper tackles the thresholding bottleneck in visual place recognition by introducing per-place thresholds automatically derived from negative Gaussian mixture statistics of non-matching images. The authors integrate this approach into a VPR pipeline and validate it across multiple descriptors (EigenPlaces, CosPlace, AlexNet) and datasets (GardensPoint, SFU, Nordland mini variants), showing improved recall, including Recall@1, over baseline thresholding. Key contributions include the negative-statistics thresholding framework, per-place threshold computation, and demonstrated generalizability across differing environmental conditions. The work reduces the need for manual threshold tuning in robotic VPR systems and has practical implications for robust real-world navigation and mapping.

Abstract

Visual place recognition (VPR) is an important component technology for camera-based mapping and navigation applications. This is a challenging problem because images of the same place may appear quite different for reasons including seasonal changes, weather illumination, structural changes to the environment, as well as transient pedestrian or vehicle traffic. Papers focusing on generating image descriptors for VPR report their results using metrics such as recall@K and ROC curves. However, for a robot implementation, determining which matches are sufficiently good is often reduced to a manually set threshold. And it is difficult to manually select a threshold that will work for a variety of visual scenarios. This paper addresses the problem of automatically selecting a threshold for VPR by looking at the 'negative' Gaussian mixture statistics for a place - image statistics indicating not this place. We show that this approach can be used to select thresholds that work well for a variety of image databases and image descriptors.