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Cross-View Localization Based on Few-Shot Learning for Mars Rover via MarsCVFP Guidance.

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Title: Cross-View Localization Based on Few-Shot Learning for Mars Rover via MarsCVFP Guidance.
Authors: Kou, Yuke1,2 (AUTHOR), Wan, Wenhui1,2 (AUTHOR) wanwh@aircas.ac.cn, Di, Kaichang1 (AUTHOR), Liu, Zhaoqin1,2 (AUTHOR), Peng, Man1 (AUTHOR), Wang, Yexin1 (AUTHOR), Xie, Bin1,2 (AUTHOR), Wang, Biao1,2 (AUTHOR), Liu, Waichung1 (AUTHOR)
Source: Remote Sensing. Feb2026, Vol. 18 Issue 4, p668. 30p.
Subjects: Mars rovers, Localization problems (Robotics), Planetary exploration, Machine learning, Feature extraction, Template matching (Digital image processing)
Abstract: Highlights: What are the main findings? We innovatively propose a two-stage cross-view localization framework for Mars rovers. The first stage incorporates a few-shot learning model, MarsCVFP, which can implicitly learn and extract cross-view invariant features on the Martian surface without relying on explicitly defined or specific learning targets. In the second stage, a modified template matching algorithm is employed to achieve robust global localization. We introduce a multi-scale feature pyramid structure (MSFPS) and a feature interaction module (FIM) to capture discriminative fine-grained features, especially in Martian environments characterized by weak textures and unstructured features. Alongside, we design a multi-resolution contrastive loss ( L C F ) within the MarsCVFP to alleviate the degradation in feature consistency extraction caused by the spatial resolution discrepancies between rover and orbiter imagery. What is the implication of the main finding? We validate our framework on 85 unit-planned sites and 20 panoramic sites traversed by the Zhurong rover. The proposed framework consistently outperforms both traditional approaches and representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains. It achieves a localization success rate above 82% while maintaining an accuracy of better than 4 pixels, even under coarse prior position uncertainties spanning 40 m × 40 m. High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40 m × 40 m. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Cross-View Localization Based on Few-Shot Learning for Mars Rover via MarsCVFP Guidance.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p668. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Mars+rovers%22">Mars rovers</searchLink><br /><searchLink fieldCode="DE" term="%22Localization+problems+%28Robotics%29%22">Localization problems (Robotics)</searchLink><br /><searchLink fieldCode="DE" term="%22Planetary+exploration%22">Planetary exploration</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Template+matching+%28Digital+image+processing%29%22">Template matching (Digital image processing)</searchLink>
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  Label: Abstract
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  Data: Highlights: What are the main findings? We innovatively propose a two-stage cross-view localization framework for Mars rovers. The first stage incorporates a few-shot learning model, MarsCVFP, which can implicitly learn and extract cross-view invariant features on the Martian surface without relying on explicitly defined or specific learning targets. In the second stage, a modified template matching algorithm is employed to achieve robust global localization. We introduce a multi-scale feature pyramid structure (MSFPS) and a feature interaction module (FIM) to capture discriminative fine-grained features, especially in Martian environments characterized by weak textures and unstructured features. Alongside, we design a multi-resolution contrastive loss ( L C F ) within the MarsCVFP to alleviate the degradation in feature consistency extraction caused by the spatial resolution discrepancies between rover and orbiter imagery. What is the implication of the main finding? We validate our framework on 85 unit-planned sites and 20 panoramic sites traversed by the Zhurong rover. The proposed framework consistently outperforms both traditional approaches and representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains. It achieves a localization success rate above 82% while maintaining an accuracy of better than 4 pixels, even under coarse prior position uncertainties spanning 40 m × 40 m. High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40 m × 40 m. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18040668
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        Text: English
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      – SubjectFull: Localization problems (Robotics)
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      – SubjectFull: Planetary exploration
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      – SubjectFull: Machine learning
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      – SubjectFull: Template matching (Digital image processing)
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