Localization and Offline Mapping of High-Voltage Substations in Rough Terrain Using a Ground Vehicle
CoRR(2024)
摘要
This paper proposes an efficient hybrid localization framework for the
autonomous navigation of an unmanned ground vehicle in uneven or rough terrain,
as well as techniques for detailed processing of 3D point cloud data. The
framework is an extended version of FAST-LIO2 algorithm aiming at robust
localization in known point cloud maps using Lidar and inertial data. The
system is based on a hybrid scheme which allows the robot to not only localize
in a pre-built map, but concurrently perform simultaneous localization and
mapping to explore unknown scenes, and build extended maps aligned with the
existing map. Our framework has been developed for the task of autonomous
ground inspection of high-voltage electrical substations residing in rough
terrain. We present the application of our algorithm in field trials, using a
pre-built map of the substation, but also analyze techniques that aim to
isolate the ground and its traversable regions, to allow the robot to approach
points of interest within the map and perform inspection tasks using visual and
thermal data.
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