Planetary Rover Localisation via Surface and Orbital Image Matching

Franchi, Valerio; Ntagiou, Evridiki V.

doi:10.1109/aero53065.2022.9843296

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…In recent years, with the rapid development of deep learning, more and more learning-based methods have been used to match PRSI [41][42][43][44]. Zhong et al [41] proposed a feature detection and description method for planetary images, which obtained a sparse and reliable set of feature points by learning the depth features of images, called Robust Planetary Features (RPFeat).…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Huang,

Wan,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Matching planetary remote sensing images (PRSI) is essential for deep space exploration. Through matching optical images collected by different probes, the terrain of planets can be accurately mapped; however, PRSI lacks surface texture information and exhibits noticeable nonlinear radiation differences, e.g., illumination differences. It is not easy to achieve satisfactory results through traditional matching methods. In order to cope with the above problems, a new PRSI matching method has been proposed. It can extract low-high frequency features by building doublefrequency scale space. In addition, we also use the nonmaximum suppression strategy for rejecting overlapped feature points, which reduces the time consumption and improves the matching accuracy. The experimental results show that the proposed method can effectively match PRSI and be superior to comparable methods.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Huang,

Wan,

Zhou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A purely data-driven approach is presented in [15], wherein a convolutional neural network is trained on synthetic data to match the rover observations with orbital imagery. Closest to our approach, [16] presents a particle filtering technique to compare rover monocular camera imagery with orbital imagery and uses a Siamese neural network approach to assign each particle a likelihood weight. The authors in [6] propose a similar approach for Lunar absolute localization known as LunarNav.…”

Section: Related Workmentioning

confidence: 99%

ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation

Cauligi¹,

Swan²,

Ono³

et al. 2023

Preprint

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There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. For example, Endurance-A proposes driving several kilometers a day in order to reach its target traverse of 2000 km in 4 years. Additionally, some of these proposed missions, including Endurance-A and rovers for Permanently Shadowed Regions (PSRs) of the moon, require autonomous driving and absolute localization in darkness. Endurance-A proposes to drive 1200 km of its total traverse at night. The lack of natural light available during these missions limits what can be used as visual landmarks and the range at which landmarks can be observed. In order for planetary rovers to traverse long-ranges, onboard absolute localization is critical to the rover's ability to maintain its planned trajectory and avoid known hazardous regions. Currently, the localization performed onboard rovers is relative to the rover's frame of reference and is performed through the integration of wheel and visual odometry and inertial measurements. To accomplish absolute localization, a "ground-in-the-loop" (GITL) operation is performed wherein a human operator matches local maps or images from onboard with orbital images and maps. This GITL operation places a limit on the distance that can be driven in a day to a few hundred meters, which is the distance that the rover can maintain acceptable localization error via relative methods. Previous work has shown that using craters as landmarks is a promising approach for performing absolute localization on the moon during the day. In this work we present a method of absolute localization that utilizes craters as landmarks and matches detected crater edges on the surface with known craters in orbital maps. We focus on a localization method based on a perception system which has an external illuminator and a stereo camera. While other methods based on lidar exist, lidar is not currently planned for deployment on the current proposed nighttime and PSR missions. In this paper, we evaluate (1) both monocular and stereo based surface crater edge detection techniques, (2) methods of scoring the crater edge matches for optimal localization, and (3) localization performance on simulated Lunar surface imagery at night. We demonstrate that this technique shows promise for maintaining absolute localization error of less than 10 m required for most planetary rover missions.

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