Simultaneous Localization and Mapping using the Geometric Projection Filter and Correspondence Graph Matching

Cédric Pradalier 1 Sepanta Sekhavat 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artifficial or natural). First, when no identifying marks are set on the landmarks, their identiffication by a robust algorithm is a complex problem which may be solved using correspondence graphs. Second, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as the simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the pre- vious approaches. Experimental results are presented using an outdoor robot car equipped with a 2D scanning laser sensor.
Document type :
Journal articles
Complete list of metadatas

Cited literature [7 references]  Display  Hide  Download

https://hal.inria.fr/inria-00182071
Contributor : Christian Laugier <>
Submitted on : Wednesday, October 24, 2007 - 6:46:37 PM
Last modification on : Wednesday, April 11, 2018 - 1:57:43 AM
Long-term archiving on : Monday, April 12, 2010 - 12:35:45 AM

File

pradalier-sekhavat-rsjar-04.pd...
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00182071, version 1

Collections

INRIA | UGA | IMAG

Citation

Cédric Pradalier, Sepanta Sekhavat. Simultaneous Localization and Mapping using the Geometric Projection Filter and Correspondence Graph Matching. Advanced Robotics, Taylor & Francis, 2004. ⟨inria-00182071⟩

Share

Metrics

Record views

385

Files downloads

289