Journal Article10.1007/S11263-007-0046-Z
Computer Vision on Mars
Larry Matthies,Mark Maimone,Andrew E. Johnson,Yang Cheng,Reg G. Willson,Carlos Y. Villalpando,Steve Goldberg,Andres Huertas,Andrew Neil Stein,Anelia Angelova +9 more
TL;DR: The design and performance of computer vision algorithms used on Mars in the NASA/JPL Mars Exploration Rover (MER) mission was a major step forward in the use ofComputer vision in space.
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Abstract: Increasing the level of spacecraft autonomy is essential for broadening the reach of solar system exploration. Computer vision has and will continue to play an important role in increasing autonomy of both spacecraft and Earth-based robotic vehicles. This article addresses progress on computer vision for planetary rovers and landers and has four main parts. First, we review major milestones in the development of computer vision for robotic vehicles over the last four decades. Since research on applications for Earth and space has often been closely intertwined, the review includes elements of both. Second, we summarize the design and performance of computer vision algorithms used on Mars in the NASA/JPL Mars Exploration Rover (MER) mission, which was a major step forward in the use of computer vision in space. These algorithms did stereo vision and visual odometry for rover navigation and feature tracking for horizontal velocity estimation for the landers. Third, we summarize ongoing research to improve vision systems for planetary rovers, which includes various aspects of noise reduction, FPGA implementation, and vision-based slip perception. Finally, we briefly survey other opportunities for computer vision to impact rovers, landers, and orbiters in future solar system exploration missions.
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Citations
Selection of the Mars Science Laboratory Landing Site
Matthew P. Golombek,John A. Grant,D. Kipp,Ashwin R. Vasavada,R. L. Kirk,Robin L. Fergason,P. Bellutta,Fred Calef,K. Larsen,Yasuhiro Katayama,Yasuhiro Katayama,Andres Huertas,Ross A. Beyer,Al Chen,T. J. Parker,B. Pollard,S. Lee,Y. Sun,R. H. Hoover,R. H. Hoover,H. L. Sladek,H. L. Sladek,John P. Grotzinger,Richard V. Welch,E. Z. Noe Dobrea,E. Z. Noe Dobrea,Joseph R. Michalski,Joseph R. Michalski,Michael M. Watkins +28 more
TL;DR: Gale Crater was selected as the Mars Science Laboratory landing site based on diversity, context, and biosignature preservation as mentioned in this paper, and the final four sites have layered sedimentary rocks with spectral evidence for phyllosilicates.
237
Visual Odometry Revisited: What Should Be Learnt?
Huangying Zhan,Chamara Saroj Weerasekera,Jia-Wang Bian,Ian Reid +3 more
- 01 May 2020
TL;DR: This work revisit the basics of VO and explore the right way for integrating deep learning with epipolar geometry and Perspective-n-Point method and design a simple but robust frame-to-frame VO algorithm (DF-VO) which outperforms pure deep learning-based and geometry-based methods.
203
Computer vision applications in construction: Current state, opportunities & challenges
Suman Paneru,Idris Jeelani +1 more
TL;DR: In this article, the authors provide an updated and categorized overview of computer vision applications in construction by examining the recent developments in the field and identifying the opportunities and challenges that future research needs to address to fully leverage the potential benefits of Computer Vision.
142
High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation
George Lentaris,Konstantinos Maragos,Ioannis Stratakos,Lazaros Papadopoulos,Odysseas Papanikolaou,Dimitrios Soudris,Manolis I. A. Lourakis,Xenophon Zabulis,David Gonzalez-Arjona,Gianluca Furano +9 more
TL;DR: This poster presents a meta-navigation system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and positioning satellites in space.
119
Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges
Ramon Gonzalez,Karl Iagnemma +1 more
TL;DR: This paper reviews and discusses this body of research in the context of planetary exploration rovers, and previously published state-of-the-art methods that have been validated through field testing are included as exemplary results.
117
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