ReviewSensors (Basel, Switzerland)2022
RANSAC for Robotic Applications: A Survey.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Comparative Evaluation of Cross-Sectional Geometric Feature Extraction Algorithms for LiDAR-Based Inclination Detection of Lattice Steel Towers.Sensors (Basel, Switzerland) · 2026Article
- A Hardware-Software Integrated PCB Image Registration Method Based on Local Adaptive KNN and SIFT.Sensors (Basel, Switzerland) · 2026Article
- Research on LiDAR-Assisted Optimization Algorithm for Terrain-Aided Navigation of eVTOL.Sensors (Basel, Switzerland) · 2025Article
- Novel RANSAC-based method for detecting and estimating externally attached marker spheres in craniomaxillofacial CT images.Quantitative imaging in medicine and surgery · 2025Article
- Optimizing plane detection in point clouds through line sampling.Scientific reports · 2025Article
- T360Fusion: Temporal 360 Multimodal Fusion for 3D Object Detection via Transformers.Sensors (Basel, Switzerland) · 2025Article
- Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey.Sensors (Basel, Switzerland) · 2025Review
- A Comprehensive Survey on the Integrity of Localization Systems.Sensors (Basel, Switzerland) · 2025Review
- BranchMatch: point cloud registration for individual apple trees with limited overlap based on local structure characteristics.Frontiers in plant science · 2025Article
- Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum.Sensors (Basel, Switzerland) · 2024Article
- LiDAR-360 RGB Camera-360 Thermal Camera Targetless Calibration for Dynamic Situations.Sensors (Basel, Switzerland) · 2024Article
- siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape.BMC genomics · 2024Article
- Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures.Sensors (Basel, Switzerland) · 2024Article
- LiDAR Dynamic Target Detection Based on Multidimensional Features.Sensors (Basel, Switzerland) · 2024Article
- A review of visual SLAM for robotics: evolution, properties, and future applications.Frontiers in robotics and AI · 2024Review
- Editorial for the Special Issue Recognition Robotics.Sensors (Basel, Switzerland) · 2023Article
- A Structure-Based Iterative Closest Point Using Anderson Acceleration for Point Clouds with Low Overlap.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.