Lidar SLAM-Mapping As A Potential Powerline Maintenance Tool

Abstract

The purpose of this master’s thesis is to determine whether it is possible to utilize non-conventional sensory systems for power line inspection during times that more conventional methods are severely restricted. To achieve this, we will utilize lidar-generated point clouds in conjunction with measurement data from inertial measurement units to create a geo-referenced set of point clouds to generate a map of the point-of-interest area. This will be achieved by conjoining raw lidar-data from a lidar sensor and using the data provided from the inertial measurement unit to merge millions of points into a one cohesive map. We will use Robot Op-erating System to achieve the evaluation, fusion and integration of the different streams of data. We will also use Google Cartographer to aid us in the SLAM-mapping of the different sensory data sources. Once a SLAM-mapped point cloud is generated, we can evaluate the accuracy of the data and the possibilities of the generated data to be used as a maintenance tool to assist in detecting and solving various problems that many electrical companies in rural Finland face during their daily business. Such problems are snapped power lines or excess object blocking the power lines. We will want to determine if a system like this could be used when it is impossible for cameras or the naked eye of a human to detect this kind of faults, for example during the night or during a storm.