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 well-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.