Autonomous Landing of UAV in Non-Cooperative Environments Based on Fusion of Monocular and Stereo Depth Estimation University of Chinese Academy of Sciences; Technology and En

Format:

Book

Conference/Event

Author/Creator:

Zhou, YiBiao, author.

Contributor:

Zhang, Bihui

Conference Name:

SAE 2024 Intelligent Urban Air Mobility Symposium (2024-09-06 : Hangzhou, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

In non-cooperative environments, unmanned aerial vehicles (UAVs) have to land without artificial markers, which is a key step towards achieving full autonomy. However, the existing vision-based schemes have the common problems of poor robustness and generalization, and the LiDAR-based schemes have the disadvantages of low resolution, high power consumption and high weight. In this paper, we propose an UAV landing system equipped with a binocular camera to preform 3D reconstruction and select the safe landing zone. The whole system only consists of a stereo camera, and the innovation of the solution is fusing the stereo matching algorithm and monocular depth estimation(MDE) model to get a robust prediction on the metric depth. The whole landing system consists of a stereo matching module, a monocular depth estimation (MDE) module, a depth fusion module, and a safe landing zone selection module. The stereo matching module uses Semi-Global Matching (SGM) algorithm to calculate the binocular disparities to get the dense metric depth of each pixel, and is deployed on GPU to meet the real-time requirements. The MDE module conducts relative depth estima-tion on the left-eye image, and is also deployed on GPU to improve the inference speed. The output of the MDE module has the advantages of high accuracy and excellent generalization but it is ambiguous in scale, and that of the stereo matching module is deterministic in scale but susceptible to illumination and moving objects. Considering the above features, the depth fusion module fuses the relative depth estimation result and the metric depth information into a robust and accurate metric depth map. With the metric depth map and camera intrinsic parameters, the safe landing zone selection module calculates the first and second order derivatives to detect the obstacles and finds the safe landing zone. In AirSim, we build a UAV Hardware-In-the-Loop (HIL) simulation system, and carry out a series of autonomous landing experiments. The results show that our landing scheme performs 3D reconstruction of the landing terrain and selects safe landing zone with high efficiency and reliability

Notes:

Vendor supplied data

Publisher Number:

2024-01-7019

Access Restriction:

Restricted for use by site license