A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

Shoichi Maeyama, Yuta Takahashi, Keigo Watanabe

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.

Original languageEnglish
Pages (from-to)1137-1149
Number of pages13
JournalAdvanced Robotics
Volume29
Issue number17
DOIs
Publication statusPublished - Sep 2 2015

Fingerprint

Mountings
Kalman filters
Kinematics
Sensors
Robots
Elevators
Extended Kalman filters
Wheels
Experiments

Keywords

  • kinematic parameters
  • mobile robot
  • SLAM
  • Unscented Kalman Filter

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Hardware and Architecture
  • Software

Cite this

A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF. / Maeyama, Shoichi; Takahashi, Yuta; Watanabe, Keigo.

In: Advanced Robotics, Vol. 29, No. 17, 02.09.2015, p. 1137-1149.

Research output: Contribution to journalArticle

@article{45f3684b526c461396e72e19cee5c13a,
title = "A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF",
abstract = "Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.",
keywords = "kinematic parameters, mobile robot, SLAM, Unscented Kalman Filter",
author = "Shoichi Maeyama and Yuta Takahashi and Keigo Watanabe",
year = "2015",
month = "9",
day = "2",
doi = "10.1080/01691864.2015.1067645",
language = "English",
volume = "29",
pages = "1137--1149",
journal = "Advanced Robotics",
issn = "0169-1864",
publisher = "Taylor and Francis Ltd.",
number = "17",

}

TY - JOUR

T1 - A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

AU - Maeyama, Shoichi

AU - Takahashi, Yuta

AU - Watanabe, Keigo

PY - 2015/9/2

Y1 - 2015/9/2

N2 - Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.

AB - Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.

KW - kinematic parameters

KW - mobile robot

KW - SLAM

KW - Unscented Kalman Filter

UR - http://www.scopus.com/inward/record.url?scp=84942104138&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942104138&partnerID=8YFLogxK

U2 - 10.1080/01691864.2015.1067645

DO - 10.1080/01691864.2015.1067645

M3 - Article

AN - SCOPUS:84942104138

VL - 29

SP - 1137

EP - 1149

JO - Advanced Robotics

JF - Advanced Robotics

SN - 0169-1864

IS - 17

ER -