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Multimodal perception and secure state estimation for robotic mobility platforms / Xinghua Liu, Rui Jiang, Badong Chen, Shuzhi Sam Ge.

By: Contributor(s): Material type: TextTextPublication details: Hoboken, NJ : Wiley-IEEE Press, [2023].Description: xvi, 208 pages : illustrations ; 24 cmISBN:
  • 9781119876014
Subject(s): Additional physical formats: Online version:: Multimodal perception and secure state estimation for robotic mobility platformsDDC classification:
  • DC 005.74 2023 L740
LOC classification:
  • TK7872.D48 L59 2023
Summary: "This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. In particular, the book discusses the two essential topics in autonomous systems: 1. secure state estimation that focuses on system robustness under cyber-attacks, and 2. multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors. Finally, the authors introduce a geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, where real-time road-constrained and heading-assisted pose estimation is achieved. The proposed geometric pose estimation has been validated using public and self-collected data and can be further extended to other kinds of sensor configurations with state and measurement constraints."--
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Item type Current library Collection Call number Status Date due Barcode
Circulation Circulation UM Digos College - LIC Circulation DC 005.74 L740 2023 (Browse shelf(Opens below)) Available 27859

Includes bibliographical references (pages 189-206) and index.

"This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. In particular, the book discusses the two essential topics in autonomous systems: 1. secure state estimation that focuses on system robustness under cyber-attacks, and 2. multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors. Finally, the authors introduce a geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, where real-time road-constrained and heading-assisted pose estimation is achieved. The proposed geometric pose estimation has been validated using public and self-collected data and can be further extended to other kinds of sensor configurations with state and measurement constraints."--

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