A framework for simultaneous localization and mapping utilizing model structure

Author

Schön, Thomas B. ; Karlsson, Rickard ; Törnqvist, David ; Gustafsson, Fredrik

Author_Institution

Linkoping Univ., Linkoping

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

8

Abstract

This contribution aims at unifying two trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (slam) applications, utilizing the FastSLAM algorithm. The second one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. An algorithm is introduced, which merges FastSLAM and MPF, and the result is an MPF algorithm for slam applications, where state vectors of higher dimensions can be used. Results using experimental data from a 3D slam development environment, fusing measurements from inertial sensors (accelerometer and gyro) and vision are presented.

Keywords

SLAM (robots); particle filtering (numerical methods); sensor fusion; tracking; vectors; FastSLAM algorithm; Rao-Blackwellized particle filter; accelerometer; gyro; inertial sensors; marginalized particle filter; model structure; positioning application; sensor fusion; simultaneous localization and mapping; state vectors; tracking application; vision; Acceleration; Accelerometers; Automatic control; Filtering; Particle filters; Particle tracking; Sensor fusion; Simultaneous localization and mapping; State estimation; Vectors; Rao-Blackwellized/marginalized particle filter; inertial sensors; sensor fusion; simultaneous localization and mapping; vision;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408198

Filename

4408198