Plane detection is a prerequisite to a wide variety of vision tasks. RANdom SAmple Consensus (RANSAC) algorithm is widely used for plane detection in point cloud data. Minimum description length (MDL) principle is used to deal with several competing hypothesis. This paper presents a new approach to the plane detection by integrating RANSAC and MDL. The method could avoid detecting wrong planes ...

A novel approach is presented in order to reject correspondence outliers between frames using the parallax-based rigidity constraint for epipolar geometry estimation. In this approach, the invariance of 3-D relative projective structure of a stationary scene over different views is exploited to eliminate outliers, mostly due to independently moving objects of a typical scene. The proposed appro...

This paper compares a new algorithm with two well-known algorithms for precise alignment of overlapping adjacent images. The new part of the algorithm is concerned with the selection of exactly matching pairs of feature points in the two images and its performance is compared with the performance of the Least Median of Square regression algorithm (LMedS), and the Random Sample Consensus (RANSAC...

Abstract. Indoor/Outdoor modeling of buildings is an important issue in the field building life cycle management. It seen as a joint process where two aspects collaborate to take advantage their semantic and geometric complementary. This global approach will allow more complete, correct, precise coherent reconstruction . The first such thus precisely register this data. lack overlap between ind...

In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits carefully suited random sampling consensus (RANSAC) methodology for selection subset inlier To aim, two fundamental properties are used: A sample represents linear combination sparse coefficien...

At present, the LiDAR ground filtering technology is very mature. There are fewer applications in 3D-object detection due to limitations of accuracy and efficiency. If can be removed quickly accurately, algorithm detect objects more accurately quickly. In order meet application requirements detection, inspired by Universal-RANSAC, we analyze detailed steps RANSAC propose a precise efficient RAN...

We present a new strategy for RANSAC sampling named BetaSAC, in reference to the beta distribution. Our proposed sampler builds a hypothesis set incrementally, selecting data points conditional on the previous data selected for the set. Such a sampling is shown to provide more suitable samples in terms of inlier ratio but also of consistency and potential to lead to an accurate parameters estim...

We have previously developed a mobile robot system which uses scale invariant visual landmarks to localize and simultaneously build a 3D map of the environment In this paper, we look at global localization, also known as the kidnapped robot problem, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive landmarks in the current f...

This paper presents a method for fitting a digital line (resp. plane) to a given set of points in a 2D (resp. 3D) image in the presence of outliers. One of the most widely used methods is Random Sample Consensus (RANSAC). However it is also known that RANSAC has a drawback: as maximum iteration number must be set, the solution may not be optimal. To overcome this problem, we present a new metho...

We introduce a method for surface reconstruction from point sets that is able to cope with noise and outliers. First, a splat-based representation is computed from the point set. A robust local 3D RANSAC-based procedure is used to filter the point set for outliers, then a local jet surface – a lowdegree surface approximation – is fitted to the inliers. Second, we extract the reconstructed surfa...