Skip to Main content Skip to Navigation
Conference papers

Online localization and mapping with moving objects detection in dynamic outdoor environments

Qadeer Baig 1 Trung-Dung Vu 1, 2, * Olivier Aycard 1, 2, *
* Corresponding author
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble [2007-2015]
2 LIG - Laboratoire d'Informatique de Grenoble [2007-2015]
Abstract : In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on dataset collected from INTERSAFE-2 demonstrator for typical scenario show the effectiveness of this technique.
Document type :
Conference papers
Complete list of metadatas
https://hal.inria.fr/hal-00785839
Contributor : Qadeer Baig <>
Submitted on : Thursday, February 7, 2013 - 10:42:22 AM
Last modification on : Thursday, July 9, 2020 - 9:44:36 AM

Identifiers

Collections

CNRS | INRIA | LIG | UGA | LIG_SIC_E-MOTION

Citation

Qadeer Baig, Trung-Dung Vu, Olivier Aycard. Online localization and mapping with moving objects detection in dynamic outdoor environments. ICCP 2009. IEEE 5th International Conference on Intelligent Computer Communication and Processing, IEEE, Aug 2009, Cluje Napoca, Romania. pp.401-408, ⟨10.1109/ICCP.2009.5284729⟩. ⟨hal-00785839⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

376

 

Contact                    Legal Notice                    Personal Data