Core Concepts
This paper presents an efficient hybrid localization framework and techniques for detailed processing of 3D point cloud data to enable autonomous navigation and inspection of high-voltage electrical substations in rough terrain using a ground vehicle.
Abstract
The 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 the FAST-LIO2 algorithm, aiming to achieve robust localization in known point cloud maps using Lidar and inertial data.
The system is based on a hybrid scheme that allows the robot to not only localize in a pre-built map, but also concurrently perform simultaneous localization and mapping to explore unknown scenes and build extended maps aligned with the existing map. The authors present the application of their algorithm in field trials, using a pre-built map of the substation, and also analyze techniques to isolate the ground and its traversable regions, allowing the robot to approach points of interest within the map and perform inspection tasks using visual and thermal data.
The key contributions of the paper include:
An extended version of FAST-LIO2 that can localize within a known environment and update the pre-built map through a hybrid scheme.
Techniques to smooth and de-noise 3D point clouds generated from real-time SLAM algorithms and extract the ground map.
Methods to determine the traversable regions of rough ground terrain for safe navigation within a high-voltage electrical substation.
Stats
The paper does not provide any specific numerical data or metrics, but focuses on the development and evaluation of the proposed localization and mapping techniques.
Quotes
The paper does not contain any direct quotes that are particularly striking or support the key logics.