Advancing Large Scale Automated Drone Inspection
A team of researchers at Luleň University of Technology in Sweden has developed a framework for visual inspections of very large 3-D infrastructures using autonomous micro aerial vehicles (MAVs). They introduce a system with advanced autonomy, deployable on a large scale for the inspection of old infrastructures. 'Aging infrastructure is becoming a significant issue for developed societies, with an increased cost and a big danger for the human inspectors performing the overall operation' George Nikolakopoulos, one of the researchers, says. "Thus, five years ago, we imagined aerial robots with arms to perform autonomous inspection and maintenance in a more affordable, fast, productive and secure way."

"Given a draft geometrical model of the infrastructure to be inspected, e.g. a box or a cone, we define with our algorithms paths for the MAVs that can guarantee for a complete visual inspection," Nikolakopoulos said. "The aerial robots fly around autonomously, collecting images while avoiding collisions. Upon landing, the information gathered is transformed into a dense 3-D reconstruction of the infrastructure."

"The major scientific and technological impact of our study is the fact that the multiple MAVs are operating in a non-human-supervised approach," Nikolakopoulos said. "They have very high level of autonomy and a full industrial manufacturing to perform the required inspection tasks. To the best of my knowledge, this is the first time ever that MAVs have been deployed for wind turbine inspection worldwide."

Nikolakopoulos and his colleagues evaluated the performance of their autonomous navigation system in a series of realistic outdoor field experiments that entailed the inspection of large-scale infrastructures. These tests primarily focused on the inspection of wind turbines, which are particularly challenging structures.

The results gathered in these preliminary evaluations are highly promising, highlighting the merits and potential of the proposed system for the inspection of large and complex infrastructures. In the future, the approach developed by Nikolakopoulos and his colleagues could be deployed on a large scale, paving the way towards fully-automated inspections using aerial robotics.

"We are now in the stage of commercializing the overall idea both as a service and platform, thus we are fully working in building our spinoff and strengthening the future financial plans," Nikolakopoulos said.

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This article presents a novel framework for performing visual inspection around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Ve- hicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial inspection system reaches high level of autonomy on a large scale, while pushing to the boundaries the real life deployment of aerial robotics. In the presented approach, the MAVs deployed for the inspection of the structure rely only on their onboard computer and sensory systems.

The developed framework envisions a modular system, combining open research challenges in the fields of localization, path planning and mapping, with an overall capability for a fast on site deployment and a reduced execution time that can repeatably perform the inspec- tion mission according to the operator needs. The architecture of the established system includes: 1) a geometry-based path planner for coverage of complex struc- tures by multiple MAVs, 2) an accurate yet flexible localization component, which provides an accurate pose estimation for the MAVs by utilizing an Ultra Wideband fused inertial estimation scheme, and 3) visual data post-processing scheme for the 3D model building.

The performance of the proposed framework has been experi- mentally demonstrated in multiple realistic outdoor field trials, all focusing on the challenging structure of a wind turbine as the main test case. The successful exper- imental results, depict the merits of the proposed autonomous navigation system as the enabling technology towards aerial robotic inspectors.