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- W3208347645 abstract "Robust and persistent global localisation is essential for the safe operation of autonomous vehicles. When operating in large and diverse urban driving environments, localisation systems are frequently exposed to environments that violate sensor and algorithm assumptions. Global localisation algorithms relying on specific sensors have known failure modes, generating position information that can be accurate and robust in suitable areas, while unreliable or unavailable in other areas. To guarantee the continuous global localisation in a large and diverse area, it is required that there are redundant sources of global observations available at any location within the area. We present a behavior tree framework that can intelligently pick the best available global localisation method from amongst visual features, lidar landmarks and GPS. Compared to our previous work[1], the visual component makes available a source of global updates in challenging environments such as underground tunnels or covered carparks using a prior map and a single front facing camera. We present a method to make a geographically registered visual feature map by incorporating scale and global adjustment, a visual SLAM component that can repeatedly localise within the prebuilt geographically consistent map, and a behavior tree framework that makes intelligent state transition decisions and supervises localisation performance. We demonstrate the localisation performance of the framework using 53 datasets from the University of Sydney Campus dataset acquired over an 18 month period." @default.
- W3208347645 created "2021-11-08" @default.
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- W3208347645 date "2021-09-19" @default.
- W3208347645 modified "2023-10-18" @default.
- W3208347645 title "Integrating Vision, Lidar and GPS Localization in a Behavior Tree Framework for Urban Autonomous Driving" @default.
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- W3208347645 doi "https://doi.org/10.1109/itsc48978.2021.9564875" @default.
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