Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).
The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.
Sample Chapter(s)
Chapter 1: Introduction (772 KB)
Chapter 2: Sparse Information Filters in SLAM (2,636 KB)
Contents:
- Introduction
- Sparse Information Filters in SLAM
- Decoupling Localization and Mapping
- D-SLAM Local Map Joining Filter
- Sparse Local Submap Joining Filter
Readership: Researchers, academics, and graduate students in robotics and automated systems.
Zhan Wang
Zhan Wang received the B.E. degree from the Harbin Institute of Technology, P.R.China, in 1998, and the Ph.D. degree in engineering from the University of Technology, Sydney (UTS), Australia, in 2007. He is currently a Chancellor's Postdoctoral Research Fellow at the ARC Centre of Excellence for Autonomous Systems at UTS. His research interests include simultaneous localization and mapping (SLAM) for mobile robots, estimation theory and computer vision. He has contributed significantly on environment mapping techniques for mobile robots, including efficient SLAM algorithms exploiting the sparse structure and a original formulation of monocular SLAM.
Dr. Shoudong Huang
Dr. Shoudong Huang was born in 1969. He received the Bachelors' and Masters' degrees in mathematics and the Ph.D. degree in automatic control from Northeastern University, China, in 1987, 1990, and 1998, respectively. He is now a Senior Lecturer in the Centre for Autonomous Systems at University of Technology, Sydney, Australia. He has made significant contribution in mobile robotic 2D and 3D simultaneous localization and mapping (SLAM) such as the proof of convergence properties and possible inconsistencies of nonlinear 2D Extended Kalman Filter based SLAM algorithm, computationally efficient 2D and 3D SLAM algorithms by local map joining, and a new parallax angle feature parametrization for monocular SLAM.
Gamini Dissanayake
Gamini Dissanayake is the James N Kirby Professor of Mechanical and Mechatronic Engineering at University of Technology, Sydney (UTS). He leads the UTS node of the Australian Research Council Centre of Excellence for Autonomous Systems (CAS), UTS Centre for Intelligent Mechatronic Systems and the UTS robotics team. His current research interests are in the areas of localization and map building for mobile robots, navigation systems, dynamics and control of mechanical systems, cargo handling, optimisation and path planning. He is recognised internationally for his pioneering work in simultaneous localisation and mapping for robots. He graduated in Mechanical/Production Engineering from the University of Peradeniya, Sri Lanka. He received his M.Sc. in Machine Tool Technology and Ph.D. in Mechanical Engineering (Robotics) from the University of Birmingham, England.
