Simultaneous Localization and Mapping: Exactly Sparse Information Filters (New Frontiers in Robotics)
| Author | : | |
| Rating | : | 4.58 (893 Votes) |
| Asin | : | 9814350311 |
| Format Type | : | paperback |
| Number of Pages | : | 208 Pages |
| Publish Date | : | 2013-10-23 |
| Language | : | English |
DESCRIPTION:
degree in engineering from the University of Technology, Sydney (UTS), Australia, in 2007. in Mechanical Engineering (Robotics) from the University of Birmingham, England. He is recognised internationally for his pioneering work in simultaneous localisation and mapping for robots. 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. About the Author Zhan Wang received the B.E. in Machine Tool Technology and Ph.D. . degree
He received his M.Sc. 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, an
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. 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.. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF)
"Nice introduction to the application of information filters in Simultaneous" according to stochasticmind. Nice introduction to the application of information filters in Simultaneous Localization and Mapping. It is not an introduction to the subject but most certainly one of the authors' PhD work turned to a book. It is well written for use of graduate students working in the area. My own thesis was not as readable so it is good for what it is.. David said Excellent. Well written. Clear and complete. Good background on SLAM. The authors work through the gory math details in a clear and easy to follow manner. Overall, one of the more clear math-ish books I've read lately.The main contributions of this book are1) good explanation of the EIF and sparse methods"Excellent" according to David. Well written. Clear and complete. Good background on SLAM. The authors work through the gory math details in a clear and easy to follow manner. Overall, one of the more clear math-ish books I've read lately.The main contributions of this book are1) good explanation of the EIF and sparse methods2) D-SLAM and extensionsExcellent Well written. Clear and complete. Good background on SLAM. The authors work through the gory math details in a clear and easy to follow manner. Overall, one of the more clear math-ish books I've read lately.The main contributions of this book are1) good explanation of the EIF and sparse methods2) D-SLAM and extensions3) Sparse Local Submap Joining AlgorithmThe authors also provide good simulation and real-world test examples.. ) Sparse Local Submap Joining AlgorithmThe authors also provide good simulation and real-world test examples.. ) D-SLAM and extensionsExcellent Well written. Clear and complete. Good background on SLAM. The authors work through the gory math details in a clear and easy to follow manner. Overall, one of the more clear math-ish books I've read lately.The main contributions of this book are1) good explanation of the EIF and sparse methods2) D-SLAM and extensions3) Sparse Local Submap Joining AlgorithmThe authors also provide good simulation and real-world test examples.. ) Sparse Local Submap Joining AlgorithmThe authors also provide good simulation and real-world test examples.