Automatic 3-D Point Cloud Classification of Urban Environments

Abstract

This paper addresses the problem of assigning a label to three-dimensional data points collected from laser scanners. We are specifically interested in the application of environment modeling for autonomous robot navigation in natural and urban terrains. To capture contextual information, we choose to work within the Markov Random Field framework. The approach used in this paper is a variant of the Associative Markov Network (AMN), extended to learn directionality in the clique potentials, resulting in a new anisotropic model that can be efficiently learned using a gradient-based method for non-differentiable function. We validate the proposed approach using data collected from different range sensors.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2008
Accession Number
ADA505846

Entities

People

  • Daniel Munoz
  • Martial Hebert
  • Nicolas Vandapel

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Automatic
  • Classification
  • Data Processing
  • Data Sets
  • Directional
  • Environment
  • Equations
  • Geometry
  • Learning
  • Machine Learning
  • Point Clouds
  • Random Variables
  • Standards
  • Supervised Machine Learning
  • Three Dimensional
  • Topology

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy
  • Directed Energy