# Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates

arXiv: https://arxiv.org/abs/2407.08162 · HTML: https://mlsanity.com/paper/2407.08162/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [TransVPR: Transformer-based place recognition with multi-level attention aggregation](https://mlsanity.com/paper/2201.02001/) (2022)
- [Data-efficient Large Scale Place Recognition with Graded Similarity Supervision](https://mlsanity.com/paper/2303.11739/) (2023)
- [Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval](https://mlsanity.com/paper/2011.12663/) (2020)
- [Learning Sequential Descriptors for Sequence-based Visual Place Recognition](https://mlsanity.com/paper/2207.03868/) (2022)
- [Probabilistic Visual Place Recognition for Hierarchical Localization](https://mlsanity.com/paper/2105.03091/) (2021)
- [What makes visual place recognition easy or hard?](https://mlsanity.com/paper/2106.12671/) (2021)
- [MRS-VPR: a multi-resolution sampling based global visual place recognition method](https://mlsanity.com/paper/1902.10059/) (2019)
- [Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition](https://mlsanity.com/paper/2112.04701/) (2021)
- [Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique](https://mlsanity.com/paper/2210.07509/) (2022)
- [Robust GPS-Vision Localization via Integrity-Driven Landmark Attention](https://mlsanity.com/paper/2101.04836/) (2021)
- [Visual Measurement Integrity Monitoring for UAV Localization](https://mlsanity.com/paper/1909.08537/) (2019)

## Built on by (most cited first, 8 of 8)

- [Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction](https://mlsanity.com/paper/2503.06840/) (2025)
- [Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition](https://mlsanity.com/paper/2602.04401/) (2026)
- [Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation](https://mlsanity.com/paper/2506.15988/) (2025)
- [YOPO-Nav: Visual Navigation using 3DGS Graphs from One-Pass Videos](https://mlsanity.com/paper/2512.09903/) (2025)
- [LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization](https://mlsanity.com/paper/2603.01839/) (2026)
- [Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark](https://mlsanity.com/paper/2603.04056/) (2026)
- [Adversarial Attacks on Robot Localization Systems via Deep Feature Perturbation](https://mlsanity.com/paper/2606.01892/) (2026)
- [Recovering from Pose-Selection Failures in Repeated Visual Localization](https://mlsanity.com/paper/2610.16409/) (2026)

## Abstract (arXiv)

Visual Place Recognition (VPR) systems often have imperfect performance, affecting the `integrity' of position estimates and subsequent robot navigation decisions. Previously, SVM classifiers have been used to monitor VPR integrity. This research introduces a novel Multi-Layer Perceptron (MLP) integrity monitor which demonstrates improved performance and generalizability, removing per-environment training and reducing manual tuning requirements. We test our proposed system in extensive real-world experiments, presenting two real-time integrity-based VPR verification methods: a single-query rejection method for robot navigation to a goal zone (Experiment 1); and a history-of-queries method that takes a best, verified, match from its recent trajectory and uses an odometer to extrapolate a current position estimate (Experiment 2). Noteworthy results for Experiment 1 include a decrease in aggregate mean along-track goal error from ~9.8m to ~3.1m, and an increase in the aggregate rate of successful mission completion from ~41% to ~55%. Experiment 2 showed a decrease in aggregate mean along-track localization error from ~2.0m to ~0.5m, and an increase in the aggregate localization precision from ~97% to ~99%. Overall, our results demonstrate the practical usefulness of a VPR integrity monitor in real-world robotics to improve VPR localization and consequent navigation performance.