Embodiedplace: Learning Mixture-of-features With Embodied Constraints For Visual Place Recognition

Abstract

Visual Place Recognition (VPR) is a scene-oriented image retrieval problem in computer vision in which re-ranking based on local features is commonly employed to improve performance. In robotics, VPR is also referred to as Loop Closure Detection, which emphasizes spatial-temporal verification within a sequence. However, designing local features specifically for VPR is impractical, and relying on motion sequences imposes limitations. Inspired by these observations, we propose a novel, simple re-ranking method that refines global features through a Mixture-of-Features (MoF) approach under embodied constraints. First, we analyze the practical feasibility of embodied constraints in VPR and categorize them according to existing datasets, which include GPS tags, sequential timestamps, local feature matching, and self-similarity matrices. We then propose a learning-based MoF weight-computation approach, utilizing a multi-metric loss function. Experiments demonstrate that our method improves t

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyliu2025embodiedplace

Related papers

• Mixvpr: Feature Mixing For Visual Place Recognition (2023)22.68
• Evaluation Of Visual Place Recognition Methods For Image Pair Retrieval In 3D Vision And Robotics (2026)0.00
• Focus On Local: Finding Reliable Discriminative Regions For Visual Place Recognition (2025)10.70
• Mutualvpr: A Mutual Learning Framework For Resolving Supervision Inconsistencies Via Adaptive Clustering (2024)0.00
• Tightly Coupled Learning Strategy For Weakly Supervised Hierarchical Place Recognition (2022)7.81
• Structvpr++: Distill Structural And Semantic Knowledge With Weighting Samples For Visual Place Recognition (2025)3.58
• Range And Bird's Eye View Fused Cross-modal Visual Place Recognition (2025)0.00
• Structured Pruning For Efficient Visual Place Recognition (2024)2.26