Accession Number:

ADA505846

Title:

Automatic 3-D Point Cloud Classification of Urban Environments

Descriptive Note:

Conference paper

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA

Personal Author(s):

• Munoz, Daniel
• Vandapel, Nicolas
• Hebert, Martial

Report Date:

2008-12-01

Pagination or Media Count:

9.0

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.

Descriptors:

• *TERRAIN MODELS
• *DATA ACQUISITION
• *URBAN AREAS
• *MARKOV PROCESSES
• *MAPPING
• ANISOTROPY
• AUTONOMOUS NAVIGATION
• COMPUTER FILES
• OPTICAL SCANNING
• DIRECTIONAL
• THREE DIMENSIONAL
• LASERS
• ROBOTS
• SCENE GENERATION
• SYMPOSIA
• STATISTICAL DATA

Subject Categories:

• Cartography and Aerial Photography
• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE