# iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images

arXiv: https://arxiv.org/abs/2209.06376 · HTML: https://mlsanity.com/paper/2209.06376/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [Learning SO(3) Equivariant Representations with Spherical CNNs](https://mlsanity.com/paper/1711.06721/) (2017)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [Lightweight Unsupervised Deep Loop Closure](https://mlsanity.com/paper/1805.07703/) (2018)
- [UAV Localization Using Autoencoded Satellite Images](https://mlsanity.com/paper/2102.05692/) (2021)
- [PHASER: a Robust and Correspondence-free Global Pointcloud Registration](https://mlsanity.com/paper/2102.02767/) (2021)
- [i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions](https://mlsanity.com/paper/2105.12883/) (2021)
- [ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization](https://mlsanity.com/paper/2207.12317/) (2022)
- [Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue](https://mlsanity.com/paper/1906.01304/) (2019)
- [MRS-VPR: a multi-resolution sampling based global visual place recognition method](https://mlsanity.com/paper/1902.10059/) (2019)

## Built on by (most cited first, 6 of 6)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization](https://mlsanity.com/paper/2401.05971/) (2024)
- [LiftFeat: 3D Geometry-Aware Local Feature Matching](https://mlsanity.com/paper/2505.03422/) (2025)
- [From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection](https://mlsanity.com/paper/2602.20630/) (2026)
- [TOL: Textual Localization with OpenStreetMap](https://mlsanity.com/paper/2604.01644/) (2026)
- [PiLoT v2: Pixel-to-Orthogonal Map Alignment for Free-view UAV Geo-localization](https://mlsanity.com/paper/2606.31098/) (2026)

## Abstract (arXiv)

The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant modality to GPS failures. However, state-of-the-art visual localization algorithms are unable to match visual data that have a significantly different appearance due to illuminations or viewpoints. This paper presents iSimLoc, a condition/viewpoint consistent hierarchical global re-localization approach. The place features of iSimLoc can be utilized to search target images under changing appearances and viewpoints. Additionally, our hierarchical global re-localization module refines in a coarse-to-fine manner, allowing iSimLoc to perform a fast and accurate estimation. We evaluate our method on one dataset with appearance variations and one dataset that focuses on demonstrating large-scale matching over a long flight in complicated environments. On our two datasets, iSimLoc achieves 88.7% and 83.8% successful retrieval rates with 1.5s inferencing time, compared to 45.8% and 39.7% using the next best method. These results demonstrate robust localization in a range of environments.