Liu, Xinghua  (Professor), <br/><br/>
Multimodal perception and secure state estimation for robotic mobility platforms / 
Xinghua Liu, Rui Jiang, Badong Chen, Shuzhi Sam Ge. 
 - Hoboken, NJ :   Wiley-IEEE Press,   [2023]. 
 - xvi, 208 pages :   illustrations ;   24 cm. 
<br/><br/> Includes bibliographical references (pages 189-206) and index.<br/> 
<br/><br/> "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."-- 
<br/><br/><label>ISBN: </label>9781119876014 
<br/><br/><label>LCCN: </label>2022039107 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Multisensor data fusion.<br/>Mobile robots.<br/>Automated vehicles.
<br/><br/><label>LC Class. No.: </label>TK7872.D48  / L59 2023 
<br/><br/><label>Dewey Class. No.: </label>DC 005.74  / L740