Micro air vehicle local pose estimation with a two-dimensional laser scanner: A case study for electric tower inspection

Abstract

Automation of inspection tasks is crucial for the development of the power industry, where micro air vehicles have shown a great potential. Self-localization in this context remains a key issue and is the main subject of this work. This article presents a methodology to obtain complete three-dimensional local pose estimates in electric tower inspection tasks with micro air vehicles, using an on-board sensor set-up consisting of a two-dimensional light detection and ranging, a barometer sensor and an inertial measurement unit. First, we present a method to track the tower's cross-sections in the laser scans and give insights on how this can be used to model electric towers. Then, we show how the popular iterative closest point algorithm, that is typically limited to indoor navigation, can be adapted to this scenario and propose two different implementations to retrieve pose information. This is complemented with attitude estimates from the inertial measurement unit measurements, based on a gain-scheduled non-linear observer formulation. An altitude observer to compensate for barometer drift is also presented. Finally, we address velocity estimation with views to feedback position control. Validations based on simulations and experimental data are presented.

Artificial Intelligence Architecture Based on Planar LiDAR Scan Data to Detect Energy Pylon Structures in a UAV Autonomous Detailed Inspection Process

“…One issue of this technique is that it demands the aircraft to keep the alignment to the pylon to obtain proper data to feed the position calculation. Also, the inclination of the measurement plane, due to the movement of the aircraft, has a significant influence on the position error calculated by the algorithm, as described in [15].…”

Editorial for special collection on the estimation and control of MAV navigation in GPS-denied cluttered environments

“…Vina and Morin 1 present a methodology to obtain complete 3D local pose estimates in electric tower inspection tasks (where GPS localization is disturbed) with MAVs, using an on-board sensor setup consisting of a 2D LiDAR, a barometer sensor and an inertial measurement unit (IMU).…”