A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/ smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper d...

The RANdom SAmple Consensus (RANSAC) algorithm proposed by Fischler and Bolles [1] is a general parameter estimation approach designed to cope with a large proportion of outliers in the input data. Unlike many of the common robust estimation techniques such as M-estimators and least-median squares that have been adopted by the computer vision community from the statistics literature, RANSAC was...

Abstract. This paper proposes a voxel-based approach for creating digital twin of an urban environment that is capable efficiently managing smart spaces. The explains the registration and localization procedure point cloud dataset, which uses KISS ICP scan combination RANSAC method initial alignment combined cloud. mobile mapping using Riegl VMX-250 serves as reference map, Velodyne scans are u...

Motivated by the intuition that critical step of localizing a 2D image in corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose first feature-based dense framework for addressing image-to-point registration problem, dubbed CorrI2P, which consists three modules, i.e., feature embedding, symmetric overlapping region detection, and pose estimation through estab...

In the fields of wind tunnel measurement and aerospace, real-time pose information aircraft is an important index. this paper, we propose a large-scale estimation system, in which depth cameras are used to scan entire model multiple directions. Using principal component analysis (PCA) featuring vectors as target coordinate system through transformation matrix for point cloud calibration aircraf...

In this paper, we propose a new method for 3D-2D registration based on SIFT and a range intensity image, which is a kind of intensity image simultaneously acquired with a range image using an active range sensor. A linear equation for the registration parameters is formulated, which is combined with displacement estimations for extrinsic and intrinsic parameters and the distortion of a camera’s...

With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-bas...

In this paper, we propose a new algorithm for automated image registration or precise correction of satellite images. We assume that ground control points used previously are stored within the system. The algorithm first applies matching between the GCP chips stored and a new image to be registered and creates new control points. An automated stereo matching based on normalized cross correlatio...

RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solutio...

Selecting salient points from two or more images for computing correspondence is a well studied problem in image analysis. This paper describes a new and effective technique for selecting these tiepoints using condition numbers, with application to image registration and mosaicking. Condition numbers are derived for point-matching methods based on minimizing windowed objective functions for 1) ...