# Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

arXiv: https://arxiv.org/abs/2103.03395 · HTML: https://mlsanity.com/paper/2103.03395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization](https://mlsanity.com/paper/1904.11932/) (2019)
- [Efficient 2D-3D Matching for Multi-Camera Visual Localization](https://mlsanity.com/paper/1809.06445/) (2018)
- [Learning Matchable Image Transformations for Long-term Metric Visual Localization](https://mlsanity.com/paper/1904.01080/) (2019)

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

- [Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations](https://mlsanity.com/paper/2206.02622/) (2022)
- [Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions](https://mlsanity.com/paper/2502.09795/) (2025)
- [Robust Visual Localization in Compute-Constrained Environments by Salient Edge Rendering and Weighted Hamming Similarity](https://mlsanity.com/paper/2509.25520/) (2025)

## Abstract (arXiv)

We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navigating and localizing itself within an area of approximately 50 x 50 m using reference images collected years earlier by another rover (R0). We propose a visual localizer that exhibits robustness to the relatively barren terrain that we expect to find in relevant areas, and to large lighting and viewpoint differences between R0 and R1. The localizer synthesizes partial renderings of a mesh built from reference R0 images and matches those to R1 images. We evaluate our method on a dataset totaling 2160 images covering the range of expected environmental conditions (terrain, lighting, approach angle). Experimental results show the effectiveness of our approach. This work informs the Mars Sample Return campaign on the choice of a site where Perseverance (R0) will place a set of sample tubes for future retrieval by another rover (R1).