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The objective of this research-study is to explore the performance of Rao-Blackwellized Particle Filters for accurate, simultaneous localization and mapping (SLAM), with Swarm Intelligent network of UAV Robots in oceanic environment. SLAM is a method for Mobile-robots such as Maritime-UAVs Robots which could be valuable and effective to build-up a map on an unknown oceanic-air environment. In this aim, a variety of methods can be implemented and suggested by scientists. The Kalman Filter, Extended Kalman Filter, and Particle Filter are well known and popular algorithm techniques. Each of the named methods, are able to investigate on SLAM problem but may have some drawbacks, with working under some assumptions, that are not always true. As an example Extended Kalman filter estimates covariance matrix, that tends to underestimate the true covariance matrix, therefore it risks becoming inconsistent in the statistical sense. Therefore, to get a good and accurate result, it is necessary to combine several methods together. That is included with Rao-Blackwellized method, with ability to construct Particle Filter and Extended Kalman Filter for SLAM scenarios. The Particle Filter technique is responsible for estimating Robot's pose as the Extended-Kalman-Filter estimates the landmarks. Generally in marine applications UAVs are used for search and rescue mission(s), damage assessment, Maritime air environmental study and Maritime security reconnaissance which in most cases they need to cover an immense area of ocean(s). Therefore, time-wise using a single UAV is inefficient and there could be a risk of mission failure as a result of UAV's energy limitations as well as diagnostic problems. So, having a team of UAVs which each one can produce and follow its own map with cooperation with the others to satisfy mission goals would be much more efficient. The paper discusses the process of implementing, modelling and Matlab-simulation of the above techniques and shows the concept benefits in further studies of ocean-air Robotics application scenarios.

Maritime UAVs' Swarm Intelligent Robot modelling and simulation using accurate SLAM method and Rao–Blackwellized particle filters