KEYWORDS: Object recognition, Point clouds, 3D modeling, Clutter, Laser range finders, Data modeling, 3D image processing, Histograms, Voxels, Matrices
Recognizing three-dimensional (3D) objects based on local feature descriptors is a highly challenging task. Existing 3D local feature descriptors rely on single-scale surface normals, which are susceptible to noise and outliers, significantly compromising their effectiveness and robustness. A multi-scale point pair normal encoding (M-POE) method for 3D object recognition is proposed. First, we introduce the M-POE descriptor, which encodes voxelized features with multi-scale normals to describe local surfaces, exhibiting strong distinctiveness and robustness against various interferences. Second, we present guided sample consensus in second-order graphs (GSAC-SOG), an extension of RANSAC that incorporates geometric constraints and reduces sampling randomness, enabling accurate estimation of the object’s six-degree-of-freedom (6-DOF) pose. Finally, a 3D object recognition method based on the M-POE descriptor is proposed. The proposed method is evaluated on five standard datasets with state-of-the-art comparisons. The results demonstrate that (1) M-POE is robust, discriminative, and efficient; (2) GSAC-SOG is robust to outliers; (3) the proposed 3D object recognition method achieves high accuracy and robustness against clutter and occlusion, with recognition rates of 99.45%, 94.21%, and 97.88% on the U3OR, Queen, and CFV datasets, respectively.
Estimating a six-degree-of-freedom pose from a set of correspondences remains a popular solution for 3D point cloud registration. The random sample consensus (RANSAC) method is a typical pose estimator for this task. However, RANSAC still suffers from several limitations including low efficiency and the sensitivity to high outlier ratios. To tackle these problems, we propose a 1-point sample consensus method. It first constructs a local reference frame for the keypoint based on multi-scale normal vectors, which allows our method to exhibit a linear time complexity. Then, we propose a novel hypothesis evaluation method that concentrates on accurate inliers and is more reliable for hypothesis evaluation. With comparisons with two RANSAC-like methods, our method manages to achieve more accurate and efficient registrations, making it a good gift for practical applications.
Mobile Laser System and Airborne Laser System can quickly collect a large quantity of urban scene point cloud data in real time, the collected point cloud data is the main source of road area extraction. However, the obtained point cloud data are redundant and unordered discrete, which are challenging for efficient classification and extraction. In order to solve these problems, we propose a road extraction method based on the difference of normal vector: 1) preprocess the data for simplifying the point cloud, making the subsequent operations more efficiently; 2) use the progressive morphological filter to obtain the ground point cloud data, and then calculate the difference of normal vector for the clusters of the point cloud to get the preliminary road area; 3) leverage the random sample consensus plane fitting method to optimize the road area. The experimental results show that the proposed method can extract the road area accurately and quickly from the urban 3D point cloud data.
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