SLAM Handbook is a comprehensive reference work covering the theoretical foundations, practical implementations, and future directions of Simultaneous Localization and Mapping technology.
The handbook addresses the need for a unified treatment of SLAM across its full scope, from core mathematical concepts to real-world applications and emerging spatial AI systems. It brings together contributions from numerous experts in the field to provide both theoretical grounding and practical guidance. The work is structured in three parts: foundational theory including factor graphs, state representations, robustness, and optimization; practical implementations across visual, LiDAR, radar, event-based, and inertial sensing modalities; and forward-looking topics integrating deep learning, differentiable rendering, dynamic environments, and semantic understanding.
The handbook suits researchers, practitioners, and students working in robotics, computer vision, autonomous systems, and spatial AI who need authoritative coverage of SLAM methods and their evolution. It functions as both a reference for specific techniques and a comprehensive survey of the field's landscape. The incremental release structure allows readers to engage with completed sections while later parts are finalized, and the public repository enables community feedback through issue and discussion boards before final publication.
The project operates as a collaborative academic effort with contributions from multiple experts, released in phases to incorporate public feedback. The repository serves as the distribution channel for a work being published through a traditional academic press, positioning it as a curated, peer-reviewed resource rather than a continuously evolving open-source project. The structured chapter organization with citation guidance indicates attention to scholarly standards and accessibility for academic use.
