5results about How to "Realize 3D reconstruction" patented technology

The invention discloses an environment situation scanning three-dimensional reconstruction method based on infrared and SLAM, and belongs to the technical field of computer vision and three-dimensional reconstruction. According to the method, thermal radiation information and environment image information of a target environment are obtained through infrared sensing and visual imaging respectively, after feature extraction and matching processing are conducted on the two kinds of information, synchronous positioning and map construction are completed, and environment pose data and environment map data are generated; a sensing uncertainty probability model is established, after multi-source data denoising is completed through Bayesian filtering, feature level fusion is carried out to obtain fusion environment data, and finally a three-dimensional reconstruction model of a target environment is constructed based on the fusion environment data. According to the method, the full-process closed loop of environment situation scanning and three-dimensional reconstruction is realized, the application limitation of single sensing information is made up, the comprehensiveness of environment sensing data and the scene adaptability of three-dimensional reconstruction are improved, and the digital reconstruction requirements in various complex environments can be met.

The application provides a three-dimensional reconstruction method based on multi-dimensional information fusion and a convolutional neural network, and comprises the following steps: acquiring polarized sub-images of a measured object under four transmission polarization directions and including multiple detection spectral bands through a multispectral polarization camera; performing image registration and joint denoising on all images; calculating polarization parameters of the measured object, combining spectral information and polarization information to calculate an azimuth angle and a zenith angle of the measured object; and acquiring surface normal vector component three-dimensional data of the measured object through a surface normal reconstruction model constructed based on a convolutional neural network and with the aid of physical prior knowledge. The method provided by the application can effectively restore the texture details of the surface of the measured object, can realize fast three-dimensional information acquisition of a measured object with a surface simultaneously having a diffuse reflection and a specular reflection characteristic in a complex indoor or outdoor scene under illumination conditions, and is helpful to expand the application range of the polarization three-dimensional reconstruction technology.

The invention provides an intelligent plant pruning mechanical arm control method and system, relates to the field of intelligent robot control, and solves the technical problems that an existing plant pruning mechanical arm system seriously depends on a plane vision image, the plant sensing precision is low, and the pruning path planning capability is poor. The method comprises the following steps: collecting multi-modal visual data of a target plant, and processing the visual data based on a three-dimensional reconstruction algorithm to generate a plant three-dimensional model; a target three-dimensional model of a target shape is obtained, difference analysis and trajectory optimization are conducted on the plant three-dimensional model and the target three-dimensional model based on the differentiable simulation technology, and a pruning path of the mechanical arm is generated; and receiving a user instruction, analyzing the user instruction through an intelligent algorithm, mapping the user instruction to the three-dimensional model, and generating and driving a mechanical arm to perform trimming operation along the trimming path.

The application discloses a deep fracture visual photoacoustic magnetic cooperative in-situ measurement device, which comprises a measurement robot placed in a borehole, a cable lowering device placed near a borehole mouth and an upper computer placed outside the borehole. The measurement robot can realize in-situ measurement of fracture networks at different positions in the borehole and has the function of autonomous travel. The cable lowering device mainly realizes depth position sensing of the measurement robot through a depth editor and realizes data communication between the measurement robot and the upper computer. The application further discloses a deep fracture visual photoacoustic magnetic cooperative in-situ measurement method. The application can realize feature area sensing search, autonomous cruise and rapid acquisition of multi-source data, and in combination with a subsequent data processing method, high-precision in-situ measurement and visualization of deep fracture networks can be realized.

The application provides a machine vision-based friction stir welding seam quality monitoring system and method, and relates to the technical field of friction stir welding. The friction stir welding seam quality monitoring system is installed in a main body of a friction stir welding device. The system comprises, which are connected in sequence, a vision module, a three-dimensional reconstruction module, a quality prediction module, a decision module and a data transmission module. The vision module, the three-dimensional reconstruction module, the quality prediction module, the decision module and the data transmission module are electrically connected. The welding seam quality monitoring method realizes three-dimensional reconstruction of the surface state of a welding seam in a welding process through the friction stir welding seam quality monitoring system, and utilizes a convolutional neural network to perform online nondestructive detection and evaluation on the surface defects, internal defects and welding quality of the friction stir welding seam, thereby filling the technical gap in online intelligent detection of internal defects of friction stir welding.