Timothy D. Barfoot – författare

A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.

Simultaneous Localization and Mapping (SLAM) is a foundational technology that enables robots and autonomous systems to map and navigate complex environments. This handbook provides a comprehensive overview of the field, bringing together more than 60 leading researchers from around the world. The book is organized into three parts. Part I introduces the mathematical and algorithmic foundations of SLAM, including estimation, optimization, and modern map representations. Part II focuses on the state of practice, covering sensor modalities and real-world systems, from inertial and visual odometry to LiDAR, radar, and multimodal SLAM. Part III explores emerging directions, highlighting the field's transition toward Spatial AI, where machines build richer spatial and semantic understanding of their environments. Designed as a unified reference for advanced students, researchers, and engineers, this handbook presents both the principles and the future of robotic spatial perception.

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.

This bookcontains the proceedings of the 10th FSR, (Field and Service Robotics) which isthe leading single-track conference on applications of robotics in challengingenvironments. The 10th FSR was held in Toronto, Canada from 23-26June 2015.  The book contains 42full-length, peer-reviewed papers organized into a variety of topics:  Aquatic, Vision, Planetary, Aerial,Underground, and Systems.The goal ofthe book and the conference is to report and encourage the development andexperimental evaluation of field and service robots, and to generate a vibrantexchange and discussion in the community. Field robots are non-factory robots, typically mobile, that operate incomplex and dynamic environments:  on theground (Earth or other planets), under the ground, underwater, in the air or inspace.  Service robots are those thatwork closely with humans to help them with their lives.  The first FSR was held in Canberra,Australia, in1997.  Since that firstmeeting, FSR has been held roughly every two years, cycling through Asia,Americas, Europe.