Efficient Topology Estimation for Large Scale Optical Mapping
Book information
Description
Large scale optical mapping methods are in great demand among scientists who study different aspects of the seabed, and have been fostered by impressive advances in the capabilities of underwater robots in gathering optical data from the seafloor. Cost and weight constraints mean that low-cost ROVs usually have a very limited number of sensors. When a low-cost robot carries out a seafloor survey using a down-looking camera, it usually follows a predefined trajectory that provides several non time-consecutive overlapping image pairs. Finding these pairs (a process known as topology estimation) is indispensable to obtaining globally consistent mosaics and accurate trajectory estimates, which are necessary for a global view of the surveyed area, especially when optical sensors are the only data source. This book contributes to the state-of-art in large area image mosaicing methods for underwater surveys using low-cost vehicles equipped with a very limited sensor suite. The main focus has been on global alignment and fast topology estimation, which are the most challenging steps in creating large area image mosaics. This book is intended to emphasise the importance of the topology estimation problem and to present different solutions using interdisciplinary approaches opening a way to further develop new strategies and methodologies.
Similar books
Proceedings of the 4th International Conference on Electrical Engineering and Control Applications: ICEECA 2019, 17–19 December 2019, Constantine, Algeria
2021 · PDF
Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2019, Volume 1
2021 · PDF
Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2019, Volume 2
2021 · PDF
Multisensor Fusion Estimation Theory and Application
2021 · PDF
Grasping in Robotics
2013 · PDF
Experimental Robotics: The 13th International Symposium on Experimental Robotics
2013 · PDF
Elektrische Biosignale in der Medizintechnik
2020 · PDF
Using Artificial Neural Networks for Analog Integrated Circuit Design Automation
2020 · PDF