Publication detail

Odometer Module for Mobile Robot with Position Error Estimation

DAVÍDEK, D. HORÁK, K. NOVÁČEK, P. KLEČKA, J.

Original Title

Odometer Module for Mobile Robot with Position Error Estimation

English Title

Odometer Module for Mobile Robot with Position Error Estimation

Type

conference paper

Language

en

Original Abstract

In this article, an odometry module for a mobile robot with probabilistic position estimation is proposed. The localization processing is based on Extended Kalman Filter (EKF) from which only prediction phase is used. This configuration is prepared for a possible later extension with Global navigation satellite system (GNSS). Scale factor correction and University of Michigan Benchmark test (UMBmark) measurements were concluded to correct model parameters of the robot. The correction outcome was tested on an experiment with simultaneous Global Positioning System (GPS) and odometry localization. The realized differential drive mobile robot has an optical encoder for each wheel. They are connected to STM32F4 development board which calculates the odometric data and sends them with a time-stamp from GPS module over the Bluetooth to PC. Visualization and control program was created to log and store the odometric data and also to set up the odometric parameters in the firmware of the microcontroller.

English abstract

In this article, an odometry module for a mobile robot with probabilistic position estimation is proposed. The localization processing is based on Extended Kalman Filter (EKF) from which only prediction phase is used. This configuration is prepared for a possible later extension with Global navigation satellite system (GNSS). Scale factor correction and University of Michigan Benchmark test (UMBmark) measurements were concluded to correct model parameters of the robot. The correction outcome was tested on an experiment with simultaneous Global Positioning System (GPS) and odometry localization. The realized differential drive mobile robot has an optical encoder for each wheel. They are connected to STM32F4 development board which calculates the odometric data and sends them with a time-stamp from GPS module over the Bluetooth to PC. Visualization and control program was created to log and store the odometric data and also to set up the odometric parameters in the firmware of the microcontroller.

Keywords

Keywords: Mobile robots, Extended Kalman filters, Odometry, Localisation error, Visualisation, Differential drive, Incremental encoders, GPS, UMBmark, Scale factor

Released

06.10.2016

ISBN

1474-6670

Periodical

Programmable devices and systems

Year of study

2016

Number

14

State

GB

Pages from

346

Pages to

351

Pages count

6

URL

Documents

BibTex


@inproceedings{BUT128852,
  author="Daniel {Davídek} and Karel {Horák} and Petr {Nováček} and Jan {Klečka}",
  title="Odometer Module for Mobile Robot with Position Error Estimation",
  annote="In this article, an odometry module for a mobile robot with probabilistic position estimation is proposed. The localization processing is based on Extended Kalman Filter (EKF) from which only prediction phase is used. This configuration is prepared for a possible later extension with Global navigation satellite system (GNSS). Scale factor correction and University of Michigan Benchmark test (UMBmark) measurements were concluded to correct model parameters of the robot. The correction outcome was tested on an experiment with simultaneous Global Positioning System (GPS) and odometry localization. The realized differential drive mobile robot has an optical encoder for each wheel. They are connected to STM32F4 development board which calculates the odometric data and sends them with a time-stamp from GPS module over the Bluetooth to PC. Visualization and control program was created to log and store the odometric data and also to set up the odometric parameters in the firmware of the microcontroller.",
  booktitle="Proceedings of 14th IFAC INTERNATIONAL CONFERENCE on PROGRAMMABLE DEVICES and EMBEDDED SYSTEMS",
  chapter="128852",
  doi="10.1016/j.ifacol.2016.12.063",
  howpublished="electronic, physical medium",
  number="14",
  year="2016",
  month="october",
  pages="346--351",
  type="conference paper"
}