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Real-time modeling of the surrounding environment is a key functionality for autonomous navigation. Bird view grid-based approaches have interesting advantages compared to feature-based ones. Methods able to encode occupancy information and to manage perception uncertainty in dynamic environments are quite well known but very few studies have been carried out on encoding semantic information in grids...
Autonomous underwater vehicle (AUV) operations are inherently bandwidth limited but increasingly data intensive. This leads to large latencies between the capture of image data and the time at which operators are able to make informed decisions using the results of a survey. As AUV endurance and reliability continue to improve, there is a greater need for realtime on-board data processing capabilities...
A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates orders of magnitude greater than highly compressed images can be transmitted acoustically, delaying that understanding until after the robot has...
An Autonomous Underwater Vehicle (AUV) needs to demonstrate a number of capabilities, in order to carry on autonomous missions with success. One of the key areas is correctly understanding the surrounding environment. However, most of the state-of-the-art approaches in labelling world information are based on the analysis of a single frame, whilst - especially in scenarios where the vehicle interact...
As intelligent vehicles become more and more capable, they must learn to navigate and localize themselves in a wide variety of environments, including GPS-denied and only crudely mapped areas. We argue that since autonomous vehicles must be able to perceive, and semantically interpret, their immediate environment, they should be able to use abstract semantic information as their sole means of localization...
This paper describes a real-time method that obtains a hybrid description of the environment (both metric and semantic) from raw data perceived by a 2D laser scanner. A set of linguistically labelled polylines allows to build a compact geometrical representation of the indoor location where a set of representative points (or features) are semantically described. These features are processed in order...
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