Guangdong University of Technology

Jie Tao

Renquan Lu

Yong Xu

Hongxia Rao

Xinyan Li

Zehui Xiao

Chang Liu

Hui Peng

Jianhong Weng

Ankang He

Yuru Guo

Tiangang Luan

Ye Kuang

Qiang Wei

Jiaye Zhou

Xiuwei Huang

The present disclosure discloses a method for attitude control of a quadrotor UAV, comprising establishing an attitude dynamics model of the quadrotor UAV, establishing a motion equation and a state-space equation of a UAV control system, determining an LADRC-CFO, and establishing a differential tracker for reducing a system overshoot.

Methods for attitude control of quadrotor unmanned aerial vehicle (UAV)

Provided is a method for improved hand-eye calibration method based on a structured light camera. The method includes: step 1: establishing a pinhole camera model, and using a depth camera to detect a three-dimensional (3D) coordinate to obtain a physical coordinate of each point in an image coordinate system with a known depth relative to a camera coordinate system; step 2: establishing a Denavit-Hartenberg (DH) model of a robotic arm, and moving the robotic arm to a determined coordinate using inverse kinematics; step 3: collecting n sets of point cloud data, applying depth scaling coefficients to the n sets of point cloud data to perform Singular Value Decomposition (SVD), and solving for an optimal depth scaling coefficient using a Nelder-Mead algorithm; and step 4: completing the hand-eye calibration of the robotic arm based on the solved optimal depth scaling coefficient. The method offers strong operability and robustness.

Methods for improved hand-eye calibration based on structured light cameras

The present invention provides a pose estimation method for an unmanned aerial vehicle based on point, line and plane feature fusion, the method comprises the following steps: S1 extracting an RGB image and a depth map by using an RGB-D camera; S2 constructing a plane parameter space (PPS) according to the depth map, and mapping the depth map from a Cartesian space to the plane parameter space (PPS); S3 constructing an inverse octree structure, comprising: reversely constructing cell nodes for the plane parameter space (PPS) according to an octree rule, and for each cell node, performing fitting by using Gaussian distribution; S4 extracting a plane feature, comprising: extracting the plane feature on the basis of degree of feature extraction algorithm of the inverse octree; S5 extracting a linear feature, comprising: extracting a linear feature on the RGB image by using LSD algorithm detection.

Pose estimation method for unmanned aerial vehicle based on point line and plane feature fusion

The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.

Cloud-edge-end cooperative control method of 5G networked unmanned aerial vehicle for security rescue

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Guangzhou, China

Jie Tao