Tongji University

Runjie Shen

Yong Wang

Bin He

Yan Chen

Zhipeng Wang

Ping Lu

Zhongpan Zhu

Yanmin Zhou

Bin Cheng

Qiqi Zhu

Shuping Song

Yimin Lou

Xudong Wang

Tunnel defect detecting method and system using unmanned aerial vehicle (UAV) are provided, and the UAV is equipped with a light-emitting diode (LED) module, a camera, a laser radar, an ultrasonic distance meter and an inertial measurement unit (IMU). The method includes: collecting images in a tunnel based on the LED module and the camera to obtain a training image set; training by using the training image set to obtain a defect detecting model, collecting real-time tunnel images, detecting suspected defects to the real-time tunnel images by the defect detecting model, obtaining pose information of the UAV based on the camera, the laser radar, the ultrasonic distance meter and the IMU to control the UAV to hover. The method can realize accurate pose estimation and defect detection in the tunnel with no GPS signals and highly symmetrical inside.

Tunnel defect detecting method and system using unmanned aerial vehicle

The embodiment of this present disclosure provides a control method of unmanned aerial vehicle (UAV) hovering in tunnel, which comprises the following steps: acquiring hovering information of hovering position of UAV; acquiring the position information of the current position of the UAV; determining flight parameters based on hovering information and position information. The flight parameters are used to control the UAV to move from the current position to the hovering position.

Method and system for hovering control of unmanned aerial vehicle in tunnel

The present disclosure relates to a detection method and system for an underground space by joint use of fixed sensors and unmanned aerial vehicle (UAV) movement detection. The detection system includes underground space sensor nodes and an underground space UAV. The underground space sensor nodes are configured to perform fixed monitoring based on an adaptive optimal layout strategy for an underground structural space. The underground space UAV is configured to calculate a first virtual force, and realize movement detection by means of a virtual force-guided path planning algorithm. The underground space UAV is configured to calculate the first virtual force based on the electronic telescopic anti-collision bars, a second virtual force based on a static perception probability and a third virtual force based on structural evolution knowledge, and realize a fixed node-guided UAV flight detection mode by means of the virtual force-guided path planning algorithm.

Detection method and system for underground space by joint use of fixed sensor and UAV movement detection

The method includes: obtaining point cloud information collected by a depth camera, laser information collected by a lidar, and motion information of an unmanned aerial vehicle (UAV); generating a raster map based on the laser information, and obtaining pose information of the UAV based on the motion information; obtaining a map model through fusing the point cloud information, the raster map, and the pose information by a Bayesian fusion method; and correcting a latest map model by feature matching based on a previous map model.

Methods and systems for modeling poor texture tunnels based on vision-lidar coupling

Disclosed is an unmanned aerial vehicle (UAV) positioning method based on a millimeter-wave radar, including a calibration stage and a positioning stage. The calibration stage includes: acquiring ground coordinates of the unmanned aerial vehicle; and extracting feature points from radar point cloud data and get the ground coordinates of the feature points. The positioning stage includes: acquiring radar point cloud data of a current frame and pre-processing; acquiring UAV motion data and fuse the data with radar point cloud data; extracting characteristic line segment from radar point cloud data; registering the characteristic line segment of the current frame with the characteristic line segment of the previous frame, and finding matching feature points and newly added feature points; and obtaining the ground coordinates of UAV and the ground coordinates of newly added feature points based on the ground coordinates of matched feature points on the map.

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Shanghai, China

Runjie Shen