# Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach

arXiv: https://arxiv.org/abs/2310.02650 · HTML: https://mlsanity.com/paper/2310.02650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Privacy Preserving Image-Based Localization](https://mlsanity.com/paper/1903.05572/) (2019)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Active Learning of Dynamics for Data-Driven Control Using Koopman Operators](https://mlsanity.com/paper/1906.05194/) (2019)
- [Active Neural Localization](https://mlsanity.com/paper/1801.08214/) (2018)
- [Autonomous Power Line Inspection with Drones via Perception-Aware MPC](https://mlsanity.com/paper/2304.00959/) (2023)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning](https://mlsanity.com/paper/2203.00549/) (2022)
- [Multi-Robot Active Mapping via Neural Bipartite Graph Matching](https://mlsanity.com/paper/2203.16319/) (2022)
- [Towards Accurate Active Camera Localization](https://mlsanity.com/paper/2012.04263/) (2020)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [3D Active Metric-Semantic SLAM](https://mlsanity.com/paper/2309.06950/) (2023)
- [A 3D Mixed Reality Interface for Human-Robot Teaming](https://mlsanity.com/paper/2310.02392/) (2023)
- [Perception-aware Path Planning](https://mlsanity.com/paper/1605.04151/) (2016)
- [Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning](https://mlsanity.com/paper/2008.03324/) (2020)
- [Benchmarking Visual Localization for Autonomous Navigation](https://mlsanity.com/paper/2203.13048/) (2022)

## Built on by (most cited first, 3 of 3)

- [Dual Latent Memory for Visual Multi-agent System](https://mlsanity.com/paper/2602.00471/) (2026)
- [Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information](https://mlsanity.com/paper/2407.15593/) (2024)
- [ActLoc: Learning to Localize on the Move via Active Viewpoint Selection](https://mlsanity.com/paper/2508.20981/) (2025)

## Abstract (arXiv)

Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes. This work investigates how active visual localization can be used to overcome such challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. We compare existing approaches in the literature with additional proposed baselines and propose a novel data-driven approach. The result demonstrates the superior performance of the data-driven approach when compared to existing methods, both in controlled simulation experiments and real-world deployment.