Fast classification of static and dynamic environment for Bayesian Occupancy Filter (BOF)

Qadeer Baig 1 Mathias Perrollaz 1 Jander Botelho 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper we present a fast motion detection technique based on laser data and odometry/imu information. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between two consecutive data grids. We plan to use the output of this work for Bayesian Occupancy Filter (BOF) framework to reduce processing time and improve the results of subsequent clustering and tracking algorithm, based on BOF. Experimental results obtained from a real demonstrator vehicle show the effectiveness of our technique
Document type :
Conference papers
Complete list of metadatas
https://hal.inria.fr/hal-00757398
Contributor : Qadeer Baig <>
Submitted on : Monday, November 26, 2012 - 5:33:35 PM
Last modification on : Friday, October 25, 2019 - 1:22:24 AM

Identifiers

  • HAL Id : hal-00757398, version 1

Collections

CNRS | INRIA | UGA | LIG | LIG_SIC_E-MOTION

Citation

Qadeer Baig, Mathias Perrollaz, Jander Botelho, Christian Laugier. Fast classification of static and dynamic environment for Bayesian Occupancy Filter (BOF). IROS12 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Jun 2012, Villamoura, Portugal, Portugal. ⟨hal-00757398⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

429

 

 

Contact

archive-ouverte@inria.fr