# Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features

arXiv: https://arxiv.org/abs/2109.04569 · HTML: https://mlsanity.com/paper/2109.04569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [Semantic Graph Based Place Recognition for 3D Point Clouds](https://mlsanity.com/paper/2008.11459/) (2020)
- [Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression](https://mlsanity.com/paper/1812.04368/) (2018)
- [Q-learning with Nearest Neighbors](https://mlsanity.com/paper/1802.03900/) (2018)
- [Active Neural Localization](https://mlsanity.com/paper/1801.08214/) (2018)
- [Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure](https://mlsanity.com/paper/2001.10249/) (2020)
- [Deep Active Localization](https://mlsanity.com/paper/1903.01669/) (2019)
- [CNN Feature boosted SeqSLAM for Real-Time Loop Closure Detection](https://mlsanity.com/paper/1704.05016/) (2017)
- [UWB/LiDAR Fusion For Cooperative Range-Only SLAM](https://mlsanity.com/paper/1811.02854/) (2018)
- [Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions](https://mlsanity.com/paper/1906.03366/) (2019)

## Built on by (most cited first, 1 of 1)

- [Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer](https://mlsanity.com/paper/2403.10552/) (2024)

## Abstract (arXiv)

Visual robot self-localization is a fundamental problem in visual robot navigation and has been studied across various problem settings, including monocular and sequential localization. However, many existing studies focus primarily on single-robot scenarios, with limited exploration into general settings involving diverse robots connected through wireless networks with constrained communication capacities, such as open-world distributed robot systems. In particular, issues related to the transfer and sharing of key knowledge, such as visual descriptions and visual vocabulary, between robots have been largely neglected. This work introduces a new self-localization framework designed for open-world distributed robot systems that maintains state-of-the-art performance while offering two key advantages: (1) it employs an unsupervised visual vocabulary model that maps to multimodal, lightweight, and transferable visual features, and (2) the visual vocabulary itself is a lightweight and communication-friendly model. Although the primary focus is on encoding monocular view images, the framework can be easily extended to sequential localization applications. By utilizing complementary similarity-preserving features -- both absolute and relative -- the framework meets the requirements for being unsupervised, multimodal, lightweight, and transferable. All features are learned and recognized using a lightweight graph neural network and scene graph. The effectiveness of the proposed method is validated in both passive and active self-localization scenarios.