Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
Not on the map. Why?
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).
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 42 references that are in the map. First posted on arXiv 5 Mar 2021.
Builds on
5 of 42 references.
References · 5
- 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 405
- 2018Night-to-Day Image Translation for Retrieval-based Localizationcited by 57
- 2019GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalizationcited by 32
- 2018Efficient 2D-3D Matching for Multi-Camera Visual Localizationcited by 10
- 2019Learning Matchable Image Transformations for Long-term Metric Visual Localizationcited by 8
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.
3 papers.
- 2022Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationscited by 0
- 2025Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditionscited by 0
- 2025Robust Visual Localization in Compute-Constrained Environments by Salient Edge Rendering and Weighted Hamming Similaritycited by 0
- 2025Robust Visual Localization in Compute-Constrained Environments by Salient Edge Rendering and Weighted Hamming Similarity29 Sep 2025
- 2025Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions13 Feb 2025
- 2022Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations6 Jun 2022