Vision and Learning in the Context of Exploratory Rovers | ETH Zürich

J. de Curtò

ISBN 10: 6203924539 / ISBN 13: 9786203924534

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

Vision and Learning in the Context of Exploratory Rovers | ETH Zürich | J. de Curtò | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203924534 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 120287371

Bibliographic Details

• Title: Vision and Learning in the Context of Exploratory Rovers | ETH Zürich
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6203924539
• ISBN 13: 9786203924534
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 161 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang’E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. We also introduce a new real-time pipeline for Simultaneous Localization and Mapping (SLAM) and Visual Inertial Odometry (VIO) in the context of planetary rovers. We leverage prior information of the location of the lander to propose an object-level SLAM approach that optimizes pose and shape of the lander together with camera trajectories of the rover. As a further refinement step, we propose to use techniques of interpolation between adjacent temporal samples; videlicet synthesizing non-existing images to improve the overall accuracy of the system. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.<div><p style="text-align: justify;">Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang’E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images.</p><p style="text-align: justify;"> </p><p style="text-align: justify;">We also introduce a new real-time pipeline for Simultaneous Localization and Mapping (SLAM) and Visual Inertial Odometry (VIO) in the context of planetary rovers. We leverage prior information of the location of the lander to propose an object-level SLAM approach that optimizes pose and shape of the lander together with camera trajectories of the rover. As a further refinement step, we propose to use techniques of interpolation between adjacent temporal samples; videlicet synthesizing non-existing images to improve the overall accuracy of the system.</p><p style="text-align: justify;"> </p><p style="text-align: justify;">The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.</p></div>