# Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer

arXiv: https://arxiv.org/abs/1803.03341 · HTML: https://mlsanity.com/paper/1803.03341/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2018.

## Built on by (most cited first, 25 of 25)

- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Zero-Shot Day-Night Domain Adaptation with a Physics Prior](https://mlsanity.com/paper/2108.05137/) (2021)
- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Visual Place Recognition: A Tutorial](https://mlsanity.com/paper/2303.03281/) (2023)
- [NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning](https://mlsanity.com/paper/2112.12785/) (2021)
- [Adversarial Training is Not Ready for Robot Learning](https://mlsanity.com/paper/2103.08187/) (2021)
- [Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching](https://mlsanity.com/paper/2102.04341/) (2021)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems](https://mlsanity.com/paper/2204.01719/) (2022)
- [Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs](https://mlsanity.com/paper/2209.11673/) (2022)
- [DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods](https://mlsanity.com/paper/2303.04025/) (2023)
- [Benchmarking Visual Localization for Autonomous Navigation](https://mlsanity.com/paper/2203.13048/) (2022)
- [Digging Into Self-Supervised Learning of Feature Descriptors](https://mlsanity.com/paper/2110.04773/) (2021)
- [Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing](https://mlsanity.com/paper/2201.03246/) (2022)
- [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://mlsanity.com/paper/2505.20503/) (2025)
- [Place Recognition under Occlusion and Changing Appearance via Disentangled Representations](https://mlsanity.com/paper/2211.11439/) (2022)
- [AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce](https://mlsanity.com/paper/2302.07953/) (2023)
- [DiffPlace: Street View Generation via Place-Controllable Diffusion Model Enhancing Place Recognition](https://mlsanity.com/paper/2602.11875/) (2026)
- [A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms](https://mlsanity.com/paper/2109.11002/) (2021)
- [Are Visual Place Recognition Models Recognizing Places or Conditions? Distractor-Augmented Evaluation and Condition Suppression](https://mlsanity.com/paper/2608.06847/) (2026)
- [AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition](https://mlsanity.com/paper/2609.04369/) (2026)

## Abstract (arXiv)

We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation.