An Open-source Library for Processing of 3D Data from Indoor Scenes

Martínez-Otzeta, José María; Mendialdua, Iñigo; Rodríguez-Moreno, Itsaso; Rodriguez, Igor; Sierra, Basilio

doi:10.5220/0010870100003122

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments with data acquired by the authors show good results and practical value in industrial settings. In [ 109 ], the authors present a Python library for segmenting assets in an industrial indoor scene. They use RANSAC for plane segmentation and then employ parallelism and perpendicularity between the detected planes, along with the sensor orientation, to find the ground, ceiling, and walls of a room.…”

Section: Applicationsmentioning

confidence: 99%

“…It implements a plane segmentation function that relies on RANSAC, although it is not very sophisticated in the current version (0.16), not permitting other geometric models apart from the plane. Open3D is the library behind indoor3D, the library for processing 3D data from indoor scenes [ 109 ]. Another Python library not mentioned so far is pyRANSAC-3D [ 129 ], employed, for example, in [ 91 ].…”

Section: Softwarementioning

confidence: 99%

See 1 more Smart Citation

RANSAC for Robotic Applications: A Survey

Martínez-Otzeta

Rodríguez-Moreno

Mendialdua

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Applicationsmentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%