William Seto
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View article: Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching Open
sponsorship: This letter was recommended for publication by Associate Editor S. Behnke and Editor S. Bernard Williams upon evaluation of the reviewers' comments. This work was supported in part by the Jet Propulsion Laboratory, California …
View article: Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching Open
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…
View article: Machine Vision based Sample-Tube Localization for Mars Sample Return
Machine Vision based Sample-Tube Localization for Mars Sample Return Open
A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on t…
View article: Rover Relocalization for Mars Sample Return by Virtual Template\n Synthesis and Matching
Rover Relocalization for Mars Sample Return by Virtual Template\n Synthesis and Matching Open
We consider the problem of rover relocalization in the context of the\nnotional Mars Sample Return campaign. In this campaign, a rover (R1) needs to\nbe capable of autonomously navigating and localizing itself within an area of\napproximat…
View article: Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision Open
Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning. Learning such mappings from unlabelled data, or imp…
View article: Adversarial Inversion: Inverse Graphics with Adversarial Priors
Adversarial Inversion: Inverse Graphics with Adversarial Priors Open
Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning. Learning such mappings from unlabelled data, or imp…