This book focuses on the LiDAR technology and its critical algorithms in the field of intelligent driving. With the rapid development of intelligent driving technology, LiDAR, as one of the crucial sensors, plays a vital role in core functionalities such as environmental perception, localization, and mapping. The book aims to provide a comprehensive and systematic learning resource for engineers and researchers working in the field of intelligent driving, as well as for technology enthusiasts interested in this area.

The necessity of this book arises from the fact that, although LiDAR technology is increasingly widely used in intelligent driving, there is a lack of books that systematically introduce LiDAR-related algorithms. The publication of this book fills this gap, providing not only an in-depth discussion of the basic principles and commercial status of LiDAR but also an exploration of key algorithms such as LiDAR-body extrinsic calibration, LiDAR-camera extrinsic calibration, ground detection, obstacle clustering, target detection, multi-target tracking, road edge detection, LiDAR odometry, LiDAR+IMU combined localization, and multi-sensor fusion localization and mapping.

The content of this book is characterized by a balance between theory and practice. It not only includes an analysis of classic algorithms but also integrates new research findings from the author's team. Each chapter starts with problem definition, research background, and the mainstream research directions, helping readers quickly grasp the research status in the field. Subsequently, the book delves into detailed explanations of representative algorithms, aiming to help readers understand the principles and application processes of the algorithms.

The main benefit readers will derive from this book is a comprehensive understanding and mastery of key algorithms for LiDAR in intelligent driving, which will assist them in applying LiDAR technology more effectively in practical work and promoting the development of intelligent driving technology. Additionally, the accompanying open-source code resources in the book will provide readers with opportunities for practical operation, enhancing the learning effect.

To better understand the content of this book, readers need to have a certain foundation in computer vision, robotics, and the basics of probability theory and linear algebra. These prerequisites will help readers understand the complex algorithms and theories covered in the book more smoothly.

Dr. Jie Haoxiang has worked as a senior algorithm engineer and technical leader in Huawei and Neusoft, and has many years of experience in the vehicle industry and intelligent driving. His research interests include perception algorithm, SLAM algorithm, control algorithm, large model of automatic vehicle and robot, etc.