Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combination of MAVs with metal detectors has not been presented yet, as it requires advanced flight capabilities. To this end, we present an autonomous system to survey challenging undulated terrain using a metal detector mounted on a 5 degrees of freedom (DOF) MAV. Based on an online estimate of the terrain, our receding-horizon planner efficiently covers the area, aligning the detector to the surface while considering the kinematic and visibility constraints of the platform. As the survey requires resilient and accurate localization in diverse terrain, we also propose a factor graph-based online fusion of GNSS, IMU, and LiDAR measurements. We validate the robustness of the solution to individual sensor degeneracy by flying under the canopy of trees and over featureless fields. A simulated ablation study shows that the proposed planner reduces coverage duration and improves trajectory smoothness. Real-world flight experiments showcase autonomous mapping of buried metallic objects in undulated and obstructed terrain.

Accurate and robust real-time map generation onboard of a fixed-wing UAV is essential for obstacle avoidance, path planning, and critical maneuvers such as autonomous take-off and landing. Due to the computational constraints, the required robustness and reliability, it remains a challenge to deploy a fixed-wing UAV with an online-capable, accurate and robust map generation framework. While photogrammetric approaches have underlying assumptions on the structure and the view of the camera, generic simultaneous localization and mapping (SLAM) approaches are computationally demanding. This paper presents a framework that uses the autopilot's state estimate as a prior for sliding window bundle adjustment and map generation. Our approach outputs an accurate geo-referenced dense point-cloud which was validated in simulation on a synthetic dataset and on two real-world scenarios based on ground control points.

Within this paper, a new fast algorithm that provides efficient solutions to the problem of inspection path planning for complex 3D structures is presented. The algorithm assumes a triangular mesh representation of the structure and employs an alternating two-step optimization paradigm to find good viewpoints that together provide full coverage and a connecting path that has low cost. In every iteration, the viewpoints are chosen such that the connection cost is reduced and, subsequently, the tour is optimized. Vehicle and sensor limitations are respected within both steps. Sample implementations are provided for rotorcraft and fixed-wing unmanned aerial systems. The resulting algorithm characteristics are evaluated using simulation studies as well as multiple real-world experimental test-cases with both vehicle types.

This paper presents the conceptual design, detailed development and flight testing of AtlantikSolar, a 5.6m-wingspan solar-powered Low-Altitude Long-Endurance (LALE) Unmanned Aerial Vehicle (UAV) designed and built at ETH Zurich. The UAV is required to provide perpetual endurance at a geographic latitude of 45°N in a 4-month window centered around June 21st. An improved conceptual design method is presented and applied to maximize the perpetual flight robustness with respect to local meteorological disturbances such as clouds or winds. Airframe, avionics hardware, state estimation and control method development for autonomous flight operations are described. Flight test results include a 12-hour flight relying solely on batteries to replicate night-flight conditions. In addition, we present flight results from Search-And-Rescue field trials where a camera and processing pod were mounted on the aircraft to create high-fidelity 3D-maps of a simulated disaster area.