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Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms (Hardcover)
Loot Price: R2,415
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Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms (Hardcover)
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Multimodal Perception and Secure State Estimation for Robotic
Mobility Platforms Enables readers to understand important new
trends in multimodal perception for mobile robotics This book
provides a novel perspective on secure state estimation and
multimodal perception for robotic mobility platforms such as
autonomous vehicles. It thoroughly evaluates filter-based secure
dynamic pose estimation approaches for autonomous vehicles over
multiple attack signals and shows that they outperform conventional
Kalman filtered results. As a modern learning resource, it contains
extensive simulative and experimental results that have been
successfully implemented on various models and real platforms. To
aid in reader comprehension, detailed and illustrative examples on
algorithm implementation and performance evaluation are also
presented. Written by four qualified authors in the field, sample
topics covered in the book include: Secure state estimation that
focuses on system robustness under cyber-attacks Multi-sensor
fusion that helps improve system performance based on the
complementary characteristics of different sensors A geometric pose
estimation framework to incorporate measurements and constraints
into a unified fusion scheme, which has been validated using public
and self-collected data How to achieve real-time road-constrained
and heading-assisted pose estimation This book will appeal to
graduate-level students and professionals in the fields of ground
vehicle pose estimation and perception who are looking for modern
and updated insight into key concepts related to the field of
robotic mobility platforms.
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