Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as the structure of these debris piles is often unknown and no prior knowledge can be leveraged. In this work, we propose a computationally efficient system that is able to reliably identify safe landing sites and autonomously perform the landing maneuver. Specifically, our algorithm computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy and energy consumption information. We first estimate dense candidate landing sites from the resulting costmap and then employ clustering to group neighboring sites into a safe landing region. Finally, a minimum-jerk trajectory is computed for landing considering the surrounding obstacles and the UAV dynamics. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.
Bosch @cite_21 introduce an approach that considers a sequence of monocular images for robust homography estimation in order to identify horizontal planar regions for landing as well as for estimating the motion of the camera. @cite_7 , the authors propose a similar technique for camera motion estimation and detection of multiple planar surfaces in complex real-world scenes. While in @cite_26 , Theodore presents an approach in which they first create a stereo range map of the terrain and then choose the landing point based on the surface slope, roughness and the proximity to obstacles. Johnson @cite_15 propose a Lidar-based approach in which an elevation map is computed from Lidar measurements, followed by thresholding the regions based on local slope and roughness of the terrain. Most recently, Hinzmann @cite_6 present a landing site detection algorithm for autonomous planes in which they first employ a binary random forest classifier to select regions with grass, followed by 3D reconstruction of the most promising regions from which hazardous factors such as terrain roughness, slope and the proximity to obstacles that obscure the landing approach are computed to determine the landing point.
@cite_26 @cite_7 @cite_21 @cite_6 @cite_15
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ABSTRACT This paper describes the flight testing and evaluation of technologies for the autonomous landing of a Yamaha RMAX helicopter at non-cooperative sites without the aid of GPS. The Yamaha RMAX used for these flight trials has been modified to include stereo cameras, a scanning laser, and an avionics payload. Machine vision stereo range mapping is used to generate an accurate terrain representation, and a safe landing area determination algorithm is used to select the safest landing point within the terrain. A machine vision self-localization system is used to measure the helicopter position during the descent and landing when GPS is unavailable. The software and hardware architecture of the flight system is presented and each system component is described. Results and lessons learned from the flight evaluation and optimization of the individual components are reported as well as the overall system performance with respect to a set of objective metrics for the autonomous landing flight trails. This paper offers a real-time approach for simultaneously determining spacecraft motion and multiple 3D planar surfaces for spacecraft safe landing. The approach contains three algorithms: a multiple homography alignment algorithm, which constructs homographies under unified epipolar geometry; a closed-form motion estimation algorithm; and a simple routine for surface slope estimation. This approach has three significant advantages: first, it works well for both a simple planar scene and more complex 3D world containing many smaller planar surfaces; second, it decomposes a very large optimization problem into much smaller sub-problems that are computationally less expensive. This computational advantage means that this approach can be exploited in real time robotic operations such as during time critical spacecraft landing. Finally, this approach is very reliable and accurate. The effectiveness of this approach is determined quantitatively through extensive simulations and qualitatively with actual images This paper presents an approach to detect safe landing areas for a flying robot, on the basis of a sequence of monocular images. The approach does not require precise position and attitude sensors: it exploits the relations between 2D image homographies and 3D planes. The combination of a robust homography estimation and of an adaptive thresholding of correlation scores between registered images yields the update of a stochastic grid, that exhibits the horizontal planar areas perceived. This grid allows the integration of data gathered at various altitudes. Results are presented throughout the article. Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing for self-governed mission completion or handling of emergency situations. In this letter, we propose a perception system addressing this challenge by detecting landing sites based on their texture and geometric shape without using any prior knowledge about the environment. The proposed method considers hazards within the landing region such as terrain roughness and slope, surrounding obstacles that obscure the landing approach path, and the local wind field that is estimated by the on-board EKF. The latter enables applicability of the proposed method on small-scale autonomous planes without landing gear. A safe approach path is computed based on the UAV dynamics, expected state estimation and actuator uncertainty, and the on-board computed elevation map. The proposed framework has been successfully tested on photo-realistic synthetic datasets and in challenging real-world environments. Hazard avoidance is a key technology for landing large payloads safely on the surface of Mars. During hazard avoidance, sensors and computing onboard the lander are utilized to detect hazards in the landing zone, autonomously select a location free of hazards, and then generate a trajectory that retargets the lander to the safe landing site. Algorithms are described for scanning lidar (light detection and ranging)-based hazard detection, safe site selection, and powered landing guidance designed for Mars landing. The performance of these algorithms is quantified using a closed-loop simulation of hazard avoidance, which includes a synthetic Martian terrain generator, a scanning lidar model, and the required hazard avoidance and powered landing guidance algorithms. Preliminary results show that the proposed hazard avoidance algorithms are effective at detecting hazards and guiding the lander to a safe landing site.