Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments

TL;DR

MuSIC addresses robustness in dynamic visual place recognition by dynamically selecting the best among multiple VPR techniques on a per-frame basis using Sequential Information Consistency (SIC). SIC computes a frame-to-frame cohesion score $\theta_k = \sum_{f=0}^{F} \max(S_{q-f, k-f-W:k-f+W})$ for top candidates and selects the match with maximal $\theta$, enabling technique-internal sequence validation. MuSIC extends this by running SIC for each technique, normalizing scores with $\hat{S}_{q,n} = (S_{q,n}-\mu)/\sigma$ and choosing the technique with the highest $\theta^t_m$, thus avoiding ground-truth requirements and brute-force fusion. Across five benchmarks, MuSIC yields higher average VPR performance (AUC about $0.85$, EP about $0.63$) than individual methods with SIC and surpasses many fusion baselines, while maintaining stable computation as map size grows. This approach offers a practical, online strategy for robust VPR in changing environments without relying on prior environmental knowledge.

Abstract

Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data. VPR is challenging due to the significant changes in a place's appearance driven by different daily illumination, seasonal weather variations and diverse viewpoints. Currently, no single VPR technique excels in every environmental condition, each exhibiting unique benefits and shortcomings, and therefore combining multiple techniques can achieve more reliable VPR performance. Present multi-method approaches either rely on online ground-truth information, which is often not available, or on brute-force technique combination, potentially lowering performance with high variance technique sets. Addressing these shortcomings, we propose a VPR system dubbed Multi-Sequential Information Consistency (MuSIC) which leverages sequential information to select the most cohesive technique on an online per-frame basis. For each technique in a set, MuSIC computes their respective sequential consistencies by analysing the frame-to-frame continuity of their top match candidates, which are then directly compared to select the optimal technique for the current query image. The use of sequential information to select between VPR methods results in an overall VPR performance increase across different benchmark datasets, while avoiding the need for extra ground-truth of the runtime environment.

Figures (5)

• Figure 1: MuSIC operating with 3 VPR techniques. For a given query image $q$, SIC analyses recent observation score vectors $S_{q-1}, S_{q-2}, S_{q-3}$ for each respective technique, outputting their sequential consistency scores. The output of SIC is then used to select which technique is used to deliver the match.
• Figure 2: SIC operating with $K=2, F=2, W=1$. The correct match for the current query $q$ is the reference place $3$. However, the reference place $8$ erroneously achieves the highest similarity. By computing the sequential consistency $\theta$ from the frame-to-frame continuity of previous similarity vectors, SIC identifies the correct match.
• Figure 3: (a) shows the usual similarity scores matrix produce, while (b) shows the sequential consistencies matrix computed by SIC (K=200, F=20, W=1)
• Figure 4: Computational time, in milliseconds, required to match a single query frame at different map sizes, excluding baseline technique computation.
• Figure 5: MuSIC Technique Selections