Shandong University

Hui Yuan

Hongji Xu

Yang Yang

Qiang Liu

Qi Jiang

Tingting Wang

Shidi Fan

Xiaojie Sun

Juan Li

Shuai Li

Lingling Pan

Min Chen

Tian Guo

Baozhen Du

Qinglong Miao

Yuanchao Yue

A laser radar-camera online calibration method and system based on a reflectance map includes: data preprocessing, generating data that meets the input of the neural network part; inputting the preprocessed data into the trained neural network; the neural network will output the edge point features of the reference image and the reflectance map, that is, the reference image and the reflectance map descriptor; by comparing the similarity of the reference image and the reflectance map descriptor, find the matching relationship between the reference image feature and the reflectance map feature point; use the mapping relationship between the reflectance map and the 3D point cloud to find the matching relationship between the reference image and the point cloud; use the EPnP algorithm to estimate the transformation matrix from the given 2D-3D point pair to complete the calibration process.

Lidar-camera online calibration method and system based on reflectance map

The invention relates to a QoX adaptive management-based context information uncertainty elimination system and a working method thereof. The system includes a sensor module, a context information acquisition and modeling module, a context threshold information preset module, an original context information detection module, an adaptive management module, an uncertainty elimination module, a context information correlation analysis module, an original context information flow reconstruction module, a composite context information flow module, a fusion and reasoning module, a context application layer adjustment module and a knowledge base.

Context information uncertainty elimination system and working method based on QoX adaptive management

A motion estimation method and a system in inter-frame prediction coding of 3D point cloud data: first, isometric sub-sampling is performed for points in pc_likely_world, then, the sampled points were divided into uniform small cubes, and the centroid of point cloud location information in each small cube was calculated, and the points closest to the centroid were found, and these points were put into pc_world_target. Finally, the corresponding point pc_world_ref is found in the reference frame, and the LMS algorithm is used to calculate the global motion matrix; by reserving the centroid points in each block, the method not only effectively removes the redundant points to reduce the computational complexity of the global motion matrix but also preserves the points that represent the characteristics of the point cloud to ensure the computational accuracy of the global motion matrix.

Centroid point search-based fast global motion matching method and system for dynamic point cloud

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Jinan, China

Hui Yuan