Next Article in Journal
Non-Contact Surface Roughness Measurement by Implementation of a Spatial Light Modulator
Previous Article in Journal
A Novel High-Performance Beam-Supported Membrane Structure with Enhanced Design Flexibility for Partial Discharge Detection
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 594; doi:10.3390/s17030594

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

Grupo de Investigación de Ingeniería de Sistemas y Automática, Andalucía Tech, Universidad de Málaga, 29071 Málaga, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Ayman F. Habib
Received: 8 February 2017 / Revised: 1 March 2017 / Accepted: 10 March 2017 / Published: 15 March 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1152 KB, uploaded 15 March 2017]   |  

Abstract

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. View Full-Text
Keywords: 3D laser scanner; spatial shape features; 3D classification; point clouds; voxels; supervised learning; neural networks; lidar; ground vehicles 3D laser scanner; spatial shape features; 3D classification; point clouds; voxels; supervised learning; neural networks; lidar; ground vehicles
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Plaza-Leiva, V.; Gomez-Ruiz, J.A.; Mandow, A.; García-Cerezo, A. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning. Sensors 2017, 17, 594.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top