# Active Domain-Invariant Self-Localization Using Ego-Centric and World-Centric Maps

arXiv: https://arxiv.org/abs/2204.10497 · HTML: https://mlsanity.com/paper/2204.10497/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [Viewpoint Invariant Dense Matching for Visual Geolocalization](https://mlsanity.com/paper/2109.09827/) (2021)
- [Active Neural Localization](https://mlsanity.com/paper/1801.08214/) (2018)
- [NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences](https://mlsanity.com/paper/2110.09004/) (2021)
- [REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs](https://mlsanity.com/paper/2108.02590/) (2021)
- [Place classification with a graph regularized deep neural network model](https://mlsanity.com/paper/1506.03899/) (2015)

## Built on by (most cited first, 2 of 2)

- [Active Semantic Localization with Graph Neural Embedding](https://mlsanity.com/paper/2305.06141/) (2023)
- [A Multi-modal Approach to Single-modal Visual Place Classification](https://mlsanity.com/paper/2305.06179/) (2023)

## Abstract (arXiv)

The training of a next-best-view (NBV) planner for visual place recognition (VPR) is a fundamentally important task in autonomous robot navigation, for which a typical approach is the use of visual experiences that are collected in the target domain as training data. However, the collection of a wide variety of visual experiences in everyday navigation is costly and prohibitive for real-time robotic applications. We address this issue by employing a novel it domain-invariant NBV planner. A standard VPR subsystem based on a convolutional neural network (CNN) is assumed to be available, and its domain-invariant state recognition ability is proposed to be transferred to train the domain-invariant NBV planner. Specifically, we divide the visual cues that are available from the CNN model into two types: the output layer cue (OLC) and intermediate layer cue (ILC). The OLC is available at the output layer of the CNN model and aims to estimate the state of the robot (e.g., the robot viewpoint) with respect to the world-centric view coordinate system. The ILC is available within the middle layers of the CNN model as a high-level description of the visual content (e.g., a saliency image) with respect to the ego-centric view. In our framework, the ILC and OLC are mapped to a state vector and subsequently used to train a multiview NBV planner via deep reinforcement learning. Experiments using the public NCLT dataset validate the effectiveness of the proposed method.