This book presents the breakthrough and cutting-edge progress for collaborative perception and mapping by proposing a novel framework of multimodal perception-relative localization¿collaborative mapping for collaborative robot systems. The organization of the book allows the readers to analyze, model and design collaborative perception technology for autonomous robots. It presents the basic foundation in the field of collaborative robot systems and the fundamental theory and technical guidelines for collaborative perception and mapping. The book significantly promotes the development of autonomous systems from individual intelligence to collaborative intelligence by providing extensive simulations and real experiments results in the different chapters. This book caters to engineers, graduate students and researchers in the fields of autonomous systems, robotics, computer vision and collaborative perception.

Reviews exhaustively the key recent research into collaborative perception and mapping for autonomous robots

Introduction.- Technical Background.- Point Registration Approach for Map Fusion.- Submap-Based Probabilistic Inconsistency Detection.- Hierarchical Map Fusion Framework with Homogeneous Sensors.- Collaborative 3D Mapping using Heterogeneous Sensors.- All-Weather Collaborative Mapping with Dynamic Objects.- Collaborative Probabilistic Semantic Mapping using CNN.