Sensor fusion of odometer, compass and beacon distance for mobile robots

Rufus Fraanje; René Beltman; Fidelis Theinert; Michiel van Osch; Teade Punter; John Bolte

doi:10.4018/IJAIML.2020010101

Sensor fusion of odometer, compass and beacon distance for mobile robots

Rufus Fraanje
, René Beltman
, Fidelis Theinert
, Michiel van Osch
, Teade Punter
, John Bolte

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

Original language	English
Number of pages	17
Journal	International Journal of Artificial Intelligence and Machine Learning
Volume	10
Issue number	1
DOIs	https://doi.org/10.4018/IJAIML.2020010101
Publication status	Published - 2020

Access to Document

10.4018/IJAIML.2020010101Licence: CC BY

https://dblp.org/rec/journals/ijaiml/FraanjeBTOPB20Licence: CC BY

Cite this

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abstract = "The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.",

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AU - Beltman, René

AU - Theinert, Fidelis

AU - van Osch, Michiel

AU - Punter, Teade

AU - Bolte, John

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Y1 - 2020

N2 - The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

AB - The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

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