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.

多模感知和機器人移動平台的安全狀態估計

本書讓讀者了解移動機器人多模感知的重要新趨勢。本書提供了一個關於機器人移動平台（如自動駕駛車輛）安全狀態估計和多模感知的新觀點。它徹底評估了基於濾波器的安全動態姿態估計方法在多種攻擊信號下的表現，並顯示它們優於傳統的卡爾曼濾波結果。

作為一個現代學習資源，本書包含了在各種模型和實際平台上成功實施的大量模擬和實驗結果。為了幫助讀者理解，本書還提供了詳細而生動的算法實施和性能評估示例。本書由該領域的四位合格作者撰寫，涵蓋的示例主題包括：

- 關注系統在網絡攻擊下的韌性的安全狀態估計
- 利用不同傳感器的互補特性來提高系統性能的多傳感器融合
- 一個幾何姿態估計框架，將測量和約束條件納入統一的融合方案，並使用公共和自收集數據進行驗證
- 如何實現實時的道路限制和頭向輔助姿態估計

本書將吸引地面車輛姿態估計和感知領域的研究生和專業人士，他們希望獲得關於機器人移動平台領域的關鍵概念的現代和更新的見解。

Xinghua Liu is a Professor with Xi'an University of Technology. His research interests are secure state estimation and control, cyber-physical systems, and artificial Intelligence.

Rui Jiang is a Staff Algorithm Engineer at the OmniVision Technologies Inc., and an Adjunct Lecturer with the National University of Singapore. His research interests are intelligent sensing, and perception for robotic systems.

Badong Chen is a Professor with Xi'an Jiaotong University. His research interests are signal processing, machine learning, artificial intelligence, neural engineering, and robotics.

Shuzhi Sam Ge is a Professor with the National University of Singapore and an honorary Director of Institute for Future, Qingdao University, China. His research interests are adaptive control, robotics, and artificial Intelligence.

邢華劉是西安理工大學的教授。他的研究興趣包括安全狀態估計和控制、物聯網系統以及人工智慧。

江睿是OmniVision Technologies Inc.的高級算法工程師，也是新加坡國立大學的兼職講師。他的研究興趣包括機器人系統的智能感知和感知。

陳巴東是西安交通大學的教授。他的研究興趣包括信號處理、機器學習、人工智慧、神經工程和機器人學。

葛書志是新加坡國立大學的教授，也是青島大學未來研究院的名譽院長。他的研究興趣包括自適應控制、機器人學和人工智慧。