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A method of automatic plane detection without random search

Journal of Southeast University(English Edition) 2003年第3期19卷 216-220页

作者：李中科杨晓辉吴乐南东南大学无线电工程系南京210096

plane detection is a prerequisite for many computer vision tasks. This paper proposes a new method which can automatically detect planes from two projective images. Firstly, we modify Scott’s feature point matching method by post-processing its result with the concept of similarity, and then get the lines matching according to feature points matching based on the approximate invariance of the features’ distribution between two images. Finally, we group all feature points into subsets in terms of their geometric relations with feature lines as initial sets to estimate homography rather than by a random search strategy (like RANSAC) as in most existing methods. The proposed method is especially suitable to detecting planes in man-made scenes. This method is validated on real images.

关键词： plane detection feature matching plane homography computer vision

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Fast Planar Surface detection in Point Clouds Using Voxel-grid-center-constrained Iterative Adaptive plane Extraction

Fast Planar Surface Detection in Point Clouds Using Voxel-gr...

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第40届中国控制会议

作者： Liyong Liu Xiaowei Tu Qinghua Yang Jingyuan Yu School of Mechanical Engineering and Automation Shanghai University

plane detection is a key pre-processing step in point clouds for many applications, such as buildings registration and automous cars. Many methods have been proposed for plane detection in point clouds, but most of them cannot meet the requirement of speedability. Therefore, in this paper, we propose a voxel-grid-center-constrained iterative adaptive plane extraction method that can meet the speedability without losing the accuracy of the detected planes. First, the point cloud is subdivided using an octree, and then a voxel grid is randomly selected for coplanar testing to find the plane in the point ***, in the plane detection phase, a voxel grid center constraint strategy is used to improve the efficiency of plane ***, an iterative adaptive plane extraction technique is used to improve the robustness. Experimental results demonstrate that the proposed method can achieve high performance on the synthesis datasets as well as on the real datasets.

关键词： plane detection point clouds voxel-grid-center-constrained iterative

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Real-time and Robust Odometry Estimation Using Depth Camera for Indoor Micro Aerial Vehicle

Real-time and Robust Odometry Estimation Using Depth Camera ...

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第26届中国控制与决策会议

作者： Zheng Fang Lei Zhang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

ISBN: (纸本)9781479937097

Real-time, robust and precise estimation of a robot’s ego-motion is a crucial requirement for higher level tasks like autonomous navigation. In this paper, a real-time and robust odometry estimation system for indoor micro aerial vehicle（MAV） is developed by only using the point cloud generated from the depth camera. First, local surface normal features are used to select points with most constraints. Then, an improved iterative closest point method is used to calculate the relative transformation, which is robust against sensor noise and outliers. To further improve the robustness of the estimation, this paper constructs a local point cloud map and compares current point cloud to the local map. Besides, ground plane is also used to simplify the 6DOF estimation problem as a 3DOF estimation problem, which not only reduces the drift but also improve the estimation speed. To validate the performance of the proposed method,we compared our method to several visual odometry methods using different kind of real dataset. The experiment results show that depth only odometry can achieve similar estimation results as state of the art visual odometry methods.

关键词： Depth Camera Point Cloud Odometry Estimation Sparse ICP Surface Normal plane detection

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