A system, article, and method of 3D reconstruction with volume-based filtering for image processing.

Method and system of 3D reconstruction with volume-based filtering for image processing

The present disclosure provides a laser point cloud positioning method and system. The method comprises: converting laser point cloud reflection value data and height value data matched with a current location of an autonomous vehicle into laser point cloud projection data in a ground plane; assigning a weight for a reflection value matching probability and a height value matching probability of the laser point cloud projection data and a laser point cloud two-dimensional grid map, and determining a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map; determining a location of the autonomous vehicle in the laser point cloud two-dimensional grid map based on a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map. The present disclosure is employed to solve the problem in the prior art that when the laser point cloud matches with the map, an undesirable matching effect is achieved by individually considering the reflection value matching or height value matching, or superimposing the two simply. The present disclosure can improve the laser point cloud positioning precision, and enhance robustness of the positioning system.

Laser point cloud positioning method and system

A system provides image undistortion in 3D reconstruction. The system receives an image produced by a sensor, and determines whether correction values are cached for the sensor, where each correction value is configured to place a corresponding pixel into a corrected location. When there are no cached correction values, the system calculates correction values for pixels in the image, generates a correction grid for the image including vertices corresponding to texture coordinates from the image, where each vertex in the correction grid includes a corresponding correction value, partitions the correction grid into partitioned grids, and caches the partitioned grids. The system then renders the image using the partitioned grids.

Real-time image undistortion for incremental 3D reconstruction

A loopback detection method based on point cloud segment matching constraint and trajectory drift optimization is proposed, which adopts real-time lidar odometer and LOAM system, but the LOAM system will have large drift after long-time mapping, so a method based on point cloud segment matching constraint and trajectory drift optimization is proposed. By segmenting, describing and matching the point cloud acquired by lidar, the loop relationship is found, and the loop is adjusted by using explicit loop closure ELCH algorithm after the loop is found, and an optimization algorithm is proposed toeliminate the local inconsistencies in the adjustment process of point cloud map. After optimization, the invention proposes a posture prediction and compensation algorithm to predict and compensatethe posture after the loop. So as to maximize the effect of reducing drift error.

A loop detection method based on point cloud segment matching constraint and trajectory drift optimization

The invention provides a pipeline three-dimensional reconstruction method and system based on deep learning, a medium and equipment, and relates to the technical field of pipeline three-dimensional reconstruction, and the method comprises the steps: learning the features of point clouds through employing a deep learning method, and at least obtaining the types of assemblies to which points belong,the radiuses of the assemblies to which the points belong and the direction vectors of the points; calculating an axis point by using the radius of the component to which the point belongs and the direction vector of the point, and clustering the axis point by combining the category label of the component to which the point belongs to obtain a candidate instance; obtaining a connection relationship among different candidate instances by using a graph-based method, and forming a graph structure by taking the components as nodes; replacing nodes in the graph with an actual three-dimensional component model to complete reconstruction of the whole pipeline. According to the method and the device, the problem that the existing pipeline three-dimensional reconstruction accuracy is relatively low is solved, the complexity of a common pipeline reconstruction problem is reduced into the combination of assembly detection and model fitting problems, and the accurate three-dimensional reconstruction of the pipeline is realized.

Pipeline three-dimensional reconstruction method and system based on deep learning, medium and equipment

A system provides 3D reconstruction using multiple pipelines that run in parallel and each include a worker and a queue of abstract jobs. The system receives a new image in a stream of images by an image processing pipeline, and performs image undistortion on the new image by the image processing pipeline to obtain an undistorted image. The system then detects features in the undistorted image by the image processing pipeline, injects a dictionary search job with the undistorted image and the features into a search pipeline, and injects an image compression job with the undistorted image into a maintenance pipeline. The system then performs multi-image feature matching by the image processing pipeline using a vocabulary tree database to obtain matched images, and injects a structure integration job with the undistorted image and the matched images into a constructive pipeline for camera position and point cloud estimation.

Real-time incremental 3d reconstruction of sensor data

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Real-time incremental 3d reconstruction of sensor data