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Review
. 2022 Dec 28;23(1):327.
doi: 10.3390/s23010327.

RANSAC for Robotic Applications: A Survey

José María Martínez-Otzeta  1 , Itsaso Rodríguez-Moreno  1 , Iñigo Mendialdua  2 , Basilio Sierra  1
Affiliations
Review

RANSAC for Robotic Applications: A Survey

José María Martínez-Otzeta et al. Sensors (Basel). .

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.

Keywords: RANSAC; feature matching; object recognition; real time; robotic systems; shape detection; transformation matrix.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The general setup of epipolar geometry. The planar region defined by the points P, c1, and c2 is the epipolar plane. The blue line is the baseline, while the two orange lines are the epipolar lines.
Figure 2
Figure 2
Visualization of a point cloud of a room.
See this image and copyright information in PMC

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