The random sample consensus (RANSAC) based algorithm is widely used in estimating the two-view geometry from image point correspondences. However, it often becomes extremely slow when the data is contaminated by a large percentage of incorrect matches. To address this problem, the paper proposes a new modification of RANSAC called LP-RANSAC that is robust to varying inlier ratios and achieves large computational savings without deterioration in accuracy. LP-RANSAC integrates the locality preserving constraint into the universal RANSAC framework, which prunes most of the unreliable correspondences before the hypothesize-and-verify loop and guides non-uniform sampling to generate and verify promising models earlier. Unlike other guided sampling strategies, the proposed method is simple to implement and does not require any prior information. Extensive experiments performed on the publicly available datasets reveal that LP-RANSAC can achieve more accurate and stable solutions at much lower computational cost (in milliseconds on standard CPU) than state-of-the-art methods, particularly when handling problems with low inlier ratios.

Two-View Geometry Estimation Using RANSAC With Locality Preserving Constraint

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.

/pdf/ransac-for-robotic-applications-a-survey-12nldcyw.pdf

RANSAC for Robotic Applications: A Survey

Point cloud registration is a research field where the spatial relationship between two or more sets of points in space is determined. Point clouds are found in multiple applications, such as laser scanning, 3D reconstruction, and time-of-flight imaging, to mention a few. This paper provides a thorough overview of recent advances in learning-based 3D point cloud registration methods with an emphasis on non-rigid transformations. In this respect, the available studies should take various challenges like noise, outliers, different deformation levels, and data incompleteness into account. Therefore, a comparison study on the quantitative assessment metrics and robustness of different approaches is discussed. Furthermore, a comparative study on available datasets is reviewed. This information will help to understand the new range of possibilities and to inspire future research directions. 

A review of non-rigid transformations and learning-based 3D point cloud registration methods

DESAC: differential evolution sample consensus algorithm for image registration

ABSTRACT Earth observation using remote sensing data is a trending research field across the globe. In this perspective, image registration is a mandatory data processing step for any kind of time series data analytics. Feature-based image registration is one of the prominent categories for superimposing multi-temporal and multi-modal images over each other. The paper provides a comprehensive and comparative survey of feature detection/description techniques for remote sensing images. In addition, outlier removal algorithms are explored in detail to generate putative keypoint correspondences for accurate transformation parameter estimation. The experiments are conducted on multiple remote sensing image pairs comprising visible, Synthetic Aperture Radar (SAR) and infrared images that provide diverse characteristics of the feature target. The co-registration accuracy is quantified for all possible combinations of feature detection/description with outlier removal, and best amalgamation is visually verified to check sub-pixel spatial alignment by swiping multi-temporal or multi-modal images over each other. It has been found that SIFT performs better in optical image registration, whereas A-KAZE feature detection has an upper edge at SAR image registration task. Marginalizing Sample Consensus (MAGSAC) and Mode Guided (MG) based outlier removal techniques achieves better pruning performance for multi-temporal optical and SAR images respectively. The comparative evaluation indicates that Motion Smoothness Constraint (MSC) optimization shows optimal performance for multi-modal remote sensing images. The best possible Root Mean Square Error (RMSE) achieved for multi-temporal optical images is 0.44 pixel whereas for multi-temporal SAR images, it is 0.46 pixels. The RMSE achieved for multi-modal images is 0.48 pixel using combination of SIFT with MSC.

Feature based remote sensing image registration techniques: a comprehensive and comparative review

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC's runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.

Hamid Hekmatian

Papers

SC-RANSAC: Spatial consistency on RANSAC