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 language | English |
|---|---|
| Pages (from-to) | 1137-1149 |
| Number of pages | 13 |
| Journal | Advanced Robotics |
| Volume | 29 |
| Issue number | 17 |
| DOIs | |
| Publication status | Published - Sep 2 2015 |
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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 journal › Article
}
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 -