2012 Special Issue: Using GNG to improve 3D feature extraction-Application to 6DoF egomotion

Authors:
Diego Viejo;Jose Garcia;Miguel Cazorla;David Gil;Magnus Johnsson
Affiliations:
Instituto de Investigación en Informática, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain;Instituto de Investigación en Informática, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain;Instituto de Investigación en Informática, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain;Instituto de Investigación en Informática, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain;Lund University Cognitive Science, Lundagard, Sweden
Venue:
Neural Networks
Year:
2012

Citing 16
Cited 1

A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Iterative point matching for registration of free-form curves and surfaces

International Journal of Computer Vision
Normal vector voting: crease detection and curvature estimation on large, noisy meshes

Graphical Models - Special issue: Processing on large polygonal meshes
Estimating surface normals in noisy point cloud data

Proceedings of the nineteenth annual symposium on Computational geometry
Using Growing Cell Structures for Surface Reconstruction

SMI '03 Proceedings of the Shape Modeling International 2003
A neural network for recovering 3D shape from erroneous and few depth maps of shaded images

Pattern Recognition Letters
A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Seeded region growing: an extensive and comparative study

Pattern Recognition Letters
Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)

The Visual Computer: International Journal of Computer Graphics
A multiple object tracking approach that combines colour and depth information using a confidence measure

Pattern Recognition Letters
An Improved Evolutionary Approach for Egomotion Estimation with a 3D TOF Camera

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Three-dimensional mapping with time-of-flight cameras

Journal of Field Robotics - Three-Dimensional Mapping, Part 2
Automatic seeded region growing for color image segmentation

Image and Vision Computing
Robust on-line model-based object detection from range images

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Unsupervised classification of dynamic obstacles in urban environments

Journal of Field Robotics
6DoF egomotion computing using 3D GNG-based reconstruction

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II

2013 Special Issue: Self-organizing adaptive map: Autonomous learning of curves and surfaces from point samples

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.