Localization is an essential basic capability for vehicles to be able to navigate autonomously on the road. This can be achieved through already available sensors and new technologies (Iidars, smart cameras). These sensors combined with highly accurate maps result in greater accuracy. In this work, the benefits of storing and reusing information in memory (in data buffers) are explored. Localization systems need to perform a high-frequency estimation, map matching, calibration and error detection. A framework composed of several processing layers is proposed and studied. A main filtering layer estimates the vehicle pose while other layers address the more complex problems. High-frequency state estimation relies on proprioceptive measurements combined with GNSS observations. Calibration is essential to obtain an accurate pose. By keeping state estimates and observations in a buffer, the observation models of these sensors can be calibrated. This is achieved using smoothed estimates in place of a ground truth. Lidars and smart cameras provide measurements that can be used for localization but raise matching issues with map features. In this work, the matching problem is addressed on a spatio-temporal window, resulting in a more detailed pictur of the environment. The state buffer is adjusted using the observations and all possible matches. Although using mapped features for localization enables to reach greater accuracy, this is only true if the map can be trusted. An approach using the post smoothing residuals has been developed to detect changes and either mitigate or reject the affected features.

This book which is the second part of two volumes on ''Control of Electrical and Electronic Systems” presents a compilation of selected contributions to the 1st International Conference on Electrical Systems & Automation. The book provides rigorous discussions, the state of the art, and recent developments in the modelling, simulation and control of power electronics, industrial systems, and embedded systems. The book will be a valuable reference for beginners, researchers, and professionals interested in control of electrical and electronic systems.

The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions.

This book presents the proceedings of the 8th international Symposium “Information Fusion and Intelligent Geographic Information Systems 2017” (IF&ICIS'2017), which took place at Shanghai Maritime University, China, from May 10 to 12, 2017. The goal of the symposium was to bring together leading global experts in the field of spatial information integration and intelligent GIS (IGIS) to exchange cutting-edge research ideas and experiences, to discuss perspectives on the fast-paced development of geospatial information theory, methods and models in order to demonstrate the latest advances in IGIS and discover new ways of collaboration. The topics focus on IGIS fundamentals, models, technologies and services in maritime research, such as underwater acoustics, radiolocation, navigation, marine energy, logistics, environmental management, seafood, safety of maritime navigation and others. In addition the book discusses the integration of IGIS technologies in the emerging field of digital humanities research.

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.