RANSAC for Robotic Applications: A Survey

Abstract

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.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…The implementation of RGB-D SLAM faced a challenge in balancing segmentation accuracy, system load, and the number of detected classes from images. This challenge was tackled using TensorRT, optimized by YOLOX for high-precision real-time object recognition ( Chang et al, 2023 ; Martínez-Otzeta et al, 2022 ). It has versatile applications in real-world robotics scenarios, including autonomous driving cars, mobile robotics, and augmented reality ( Zhang and Li, 2023 ; Bahraini et al, 2018 ); see Table 2 .…”

“…Its usefulness extends to essential tasks such as mapping and odometry, providing researchers with a considerable volume of data for testing SLAM algorithms across diverse robotic applications ( Ji et al, 2021 ; End et al, 2012 ). The adaptability of these datasets is remarkable, as they find application in mobile robotics and handheld platforms, demonstrating effectiveness in both indoor and outdoor environments ( Martínez-Otzeta et al, 2022 ; Son et al, 2023 ).…”

A Structure-Based Iterative Closest Point Using Anderson Acceleration for Point Clouds with Low Overlap

“…Many effective strategies have been developed to reject outliers in the correspondences. RANSAC [ 29 ] and its variants are commonly used to this end. In the recent, graph-based methods have been widely applied to model fitting.…”

An SVD-based turntable calibration method with multiple reference planes segmented from point clouds

“…The RANSAC was developed more than forty years ago [23] and has since become the most widespread method to estimate the underlying parameters of a model. Besides its simplicity, RANSAC can tolerate a tremendous level of contamination while providing reliable parameter estimates even when well over half the data consists of outliers [24,25]. However, the point cloud grabbed from the camera may not always represent accurate geometry characteristics of the object, even when the latter is in the simplest shape.…”

“…Using the proposed calibration method and the data obtained in section 4, we calibrate the turntable and list the results in table 5. By (25) the corrected rotation angles are calculated and listed in table 6. Compared with the planned rotation angle (15 • ), the error can be as large as 0.4111 • .…”