LiDAR Technology for Intelligent Driving

Beschreibung

This book focuses on LiDAR technology and its critical algorithms for intelligent driving. With the booming advancement of intelligent driving, LiDAR, as an essential sensor, undertakes core tasks including environmental perception, localization and mapping. Designed for engineers, researchers and technology enthusiasts in this field, it serves as a comprehensive and systematic reference on LiDAR-related technologies.

While LiDAR has been widely adopted in intelligent driving, systematic books dedicated to its core algorithms remain insufficient. This book fills the gap, elaborating on LiDAR’s basic principles and commercial application status, as well as covering a full set of key algorithms: LiDAR 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 book strikes a good balance between theory and practice. It combines classic algorithm analysis with the latest research achievements from the author’s team. Each chapter begins with problem definition, research background and mainstream research directions to help readers quickly grasp field developments, followed by in-depth explanations of representative algorithms, enabling readers to fully understand algorithm principles and practical applications.

Readers can systematically master core LiDAR algorithms for intelligent driving to optimize practical engineering applications and facilitate the technological progress of intelligent driving. The matched open-source codes support hands-on practice and greatly improve learning effectiveness. A basic knowledge of computer vision, robotics, probability theory and linear algebra is recommended for smooth understanding of the book’s theories and algorithms.

This book focuses on LiDAR technology and its critical algorithms for intelligent driving. With the booming advancement of intelligent driving, LiDAR, as an essential sensor, undertakes core tasks including environmental perception, localization and mapping. Designed for engineers, researchers and technology enthusiasts in this field, it serves as a comprehensive and systematic reference on LiDAR-related technologies.

While LiDAR has been widely adopted in intelligent driving, systematic books dedicated to its core algorithms remain insufficient. This book fills the gap, elaborating on LiDAR’s basic principles and commercial application status, as well as covering a full set of key algorithms: LiDAR 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 book strikes a good balance between theory and practice. It combines classic algorithm analysis with the latest research achievements from the author’s team. Each chapter begins with problem definition, research background and mainstream research directions to help readers quickly grasp field developments, followed by in-depth explanations of representative algorithms, enabling readers to fully understand algorithm principles and practical applications.

Readers can systematically master core LiDAR algorithms for intelligent driving to optimize practical engineering applications and facilitate the technological progress of intelligent driving. The matched open-source codes support hands-on practice and greatly improve learning effectiveness. A basic knowledge of computer vision, robotics, probability theory and linear algebra is recommended for smooth understanding of the book’s theories and algorithms.

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Haoxiang Jie

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