Paper Info
Title
A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
Abstract
We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available.
Year
DOI
Venue
2014
10.1109/ICRA.2014.6907054
Robotics and Automation
Keywords
Field
DocType
SLAM (robots),distance measurement,image colour analysis,image reconstruction,image sequences,3D reconstruction,ICL-NUIM dataset,Imperial College London and National University of Ireland Maynooth dataset,RGB-D sequences,RGB-D visual odometry,SLAM trajectory estimation,benchmark,depth data,ground truth surface model,handheld RGB-D camera sequences,sensor noise modelling,surface reconstruction accuracy,synthetic imagery,visual odometry evaluation
Computer vision,Surface reconstruction,Visual odometry,Ground truth,RGB color model,Artificial intelligence,Engineering,Surfel,Trajectory,Robotics,3D reconstruction
Conference
Volume
Issue
ISSN
2014
1
1050-4729
Citations 
PageRank 
References 
169
4.38
11
Authors
4
Search Limit
100169
Name
Order
Citations
PageRank
Ankur Handa147926.11
Thomas Whelan246516.31
John McDonald32897.55
Andrew J. Davison46707350.85