Robotic Navigation and Mapping With Radar

Focusing on autonomous robotic applications, this cutting-edge resource offer you a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) an area referred to as the "Holy Grail" of autonomous robotic research. The book derives an extended Kalman Filter SLAM algorithm which exploits the penetrating ability of radar. This algorithm allows for the observation of visually occluded objects, as well as the usual directly observed objects, which contributes to a robot's position and the map state update. Moreover, you discover how the Random Finite Set (RFS) provides a more appropriate approach for representing radar based maps than conventional frameworks.

Martin Adams is a professor in the department of electrical engineering and a member of the Advanced Mining Technology Centre (AMTC) at the University of Chile. He received his D.Phil. in engineering science at the University of Oxford. John Mullane is a research scientist at Projective Space Pte. Ltd., Singapore. He holds a Ph.D. in electrical and electronic engineering from Nanyang Technological University. Ehi Jose is a research scientist at Projective Space Pte. Ltd., Singapore. He holds a Ph.D. in electrical and electronic engineering from Nanyang Technological University. Ba-Ngu Vo is a professor at the School of Electrical, Electronic and Computer Engineering at the University of Western Australia. He received his Ph.D. in engineering from Curtin University.