Abstract
This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) method using both position and orientation information of ceiling lamps. Conventional approaches used corner or line features as landmarks in their SLAM algorithms, but these methods were often unable to achieve stable navigation due to a lack of reliable visual features on the ceiling. Since lamp features are usually placed some distances from each other in indoor environments, they can be robustly detected and used as reliable landmarks. We used both the position and orientation of a lamp feature to accurately estimate the robot pose. Its orientation is obtained by calculating the principal axis from the pixel distribution of the lamp area. Both corner and lamp features are used as landmarks in the EKF (Extended Kalman Filter) to increase the stability of the SLAM process. Experimental results show that the proposed scheme works successfully in various indoor environments.
| Original language | English |
|---|---|
| Pages (from-to) | 164-170 |
| Number of pages | 7 |
| Journal | Journal of Institute of Control, Robotics and Systems |
| Volume | 17 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2011 Feb 1 |
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Keywords
- Ceiling
- Mobile robot
- Monocular camera
- SLAM
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Applied Mathematics
Cite this
Monocular vision and odometry-based SLAM using position and orientation of ceiling lamps. / Hwang, Seo Yeon; Song, Jae-Bok.
In: Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 2, 01.02.2011, p. 164-170.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Monocular vision and odometry-based SLAM using position and orientation of ceiling lamps
AU - Hwang, Seo Yeon
AU - Song, Jae-Bok
PY - 2011/2/1
Y1 - 2011/2/1
N2 - This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) method using both position and orientation information of ceiling lamps. Conventional approaches used corner or line features as landmarks in their SLAM algorithms, but these methods were often unable to achieve stable navigation due to a lack of reliable visual features on the ceiling. Since lamp features are usually placed some distances from each other in indoor environments, they can be robustly detected and used as reliable landmarks. We used both the position and orientation of a lamp feature to accurately estimate the robot pose. Its orientation is obtained by calculating the principal axis from the pixel distribution of the lamp area. Both corner and lamp features are used as landmarks in the EKF (Extended Kalman Filter) to increase the stability of the SLAM process. Experimental results show that the proposed scheme works successfully in various indoor environments.
AB - This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) method using both position and orientation information of ceiling lamps. Conventional approaches used corner or line features as landmarks in their SLAM algorithms, but these methods were often unable to achieve stable navigation due to a lack of reliable visual features on the ceiling. Since lamp features are usually placed some distances from each other in indoor environments, they can be robustly detected and used as reliable landmarks. We used both the position and orientation of a lamp feature to accurately estimate the robot pose. Its orientation is obtained by calculating the principal axis from the pixel distribution of the lamp area. Both corner and lamp features are used as landmarks in the EKF (Extended Kalman Filter) to increase the stability of the SLAM process. Experimental results show that the proposed scheme works successfully in various indoor environments.
KW - Ceiling
KW - Mobile robot
KW - Monocular camera
KW - SLAM
UR - http://www.scopus.com/inward/record.url?scp=84857352298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857352298&partnerID=8YFLogxK
U2 - 10.5302/J.ICROS.2011.17.2.164
DO - 10.5302/J.ICROS.2011.17.2.164
M3 - Article
VL - 17
SP - 164
EP - 170
JO - Journal of Institute of Control, Robotics and Systems
JF - Journal of Institute of Control, Robotics and Systems
SN - 1976-5622
IS - 2
ER -