# Multi-Platform Teach-and-Repeat Navigation by Visual Place Recognition Based on Deep-Learned Local Features

arXiv: https://arxiv.org/abs/2503.13090 · HTML: https://mlsanity.com/paper/2503.13090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [There's No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs During GPS Failure](https://mlsanity.com/paper/1809.05757/) (2018)
- [Navigation without localisation: reliable teach and repeat based on the convergence theorem](https://mlsanity.com/paper/1711.05348/) (2017)
- [Keeping an Eye on Things: Deep Learned Features for Long-Term Visual Localization](https://mlsanity.com/paper/2109.04041/) (2021)
- [On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features](https://mlsanity.com/paper/2410.18573/) (2024)
- [Monocular Visual Teach and Repeat Aided by Local Ground Planarity](https://mlsanity.com/paper/1707.08989/) (2017)

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

- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)

## Abstract (arXiv)

Uniform and variable environments still remain a challenge for stable visual localization and mapping in mobile robot navigation. One of the possible approaches suitable for such environments is appearance-based teach-and-repeat navigation, relying on simplified localization and reactive robot motion control - all without a need for standard mapping. This work brings an innovative solution to such a system based on visual place recognition techniques. Here, the major contributions stand in the employment of a new visual place recognition technique, a novel horizontal shift computation approach, and a multi-platform system design for applications across various types of mobile robots. Secondly, a new public dataset for experimental testing of appearance-based navigation methods is introduced. Moreover, the work also provides real-world experimental testing and performance comparison of the introduced navigation system against other state-of-the-art methods. The results confirm that the new system outperforms existing methods in several testing scenarios, is capable of operation indoors and outdoors, and exhibits robustness to day and night scene variations.