Abstract
In this paper, we present a new indoor 'simultaneous localization and mapping' (SLAM) technique based on an upward-looking ceiling camera. Adapted from our previous work [17], the proposed method employs sparsely-distributed line and point landmarks in an indoor environment to aid with data association and reduce extended Kalman filter computation as compared with earlier techniques. Further, the proposed method exploits geometric relationships between the two types of landmarks to provide added information about the environment. This geometric information is measured with an upward-looking ceiling camera and is used as a constraint in Kalman filtering. The performance of the proposed ceiling-view (CV) SLAM is demonstrated through simulations and experiments. The proposed method performs localization and mapping more accurately than those methods that use the two types of landmarks without taking into account their relative geometries.
| Original language | English |
|---|---|
| Article number | 4 |
| Journal | International Journal of Advanced Robotic Systems |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2014 Jan 28 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Science Applications
- Artificial Intelligence
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An efficient ceiling-view SLAM using relational constraints between landmarks. / Choi, Hyukdoo; Kim, Ryunseok; Kim, Euntai.
In: International Journal of Advanced Robotic Systems, Vol. 11, No. 1, 4, 28.01.2014.Research output: Contribution to journal › Article
TY - JOUR
T1 - An efficient ceiling-view SLAM using relational constraints between landmarks
AU - Choi, Hyukdoo
AU - Kim, Ryunseok
AU - Kim, Euntai
PY - 2014/1/28
Y1 - 2014/1/28
N2 - In this paper, we present a new indoor 'simultaneous localization and mapping' (SLAM) technique based on an upward-looking ceiling camera. Adapted from our previous work [17], the proposed method employs sparsely-distributed line and point landmarks in an indoor environment to aid with data association and reduce extended Kalman filter computation as compared with earlier techniques. Further, the proposed method exploits geometric relationships between the two types of landmarks to provide added information about the environment. This geometric information is measured with an upward-looking ceiling camera and is used as a constraint in Kalman filtering. The performance of the proposed ceiling-view (CV) SLAM is demonstrated through simulations and experiments. The proposed method performs localization and mapping more accurately than those methods that use the two types of landmarks without taking into account their relative geometries.
AB - In this paper, we present a new indoor 'simultaneous localization and mapping' (SLAM) technique based on an upward-looking ceiling camera. Adapted from our previous work [17], the proposed method employs sparsely-distributed line and point landmarks in an indoor environment to aid with data association and reduce extended Kalman filter computation as compared with earlier techniques. Further, the proposed method exploits geometric relationships between the two types of landmarks to provide added information about the environment. This geometric information is measured with an upward-looking ceiling camera and is used as a constraint in Kalman filtering. The performance of the proposed ceiling-view (CV) SLAM is demonstrated through simulations and experiments. The proposed method performs localization and mapping more accurately than those methods that use the two types of landmarks without taking into account their relative geometries.
UR - http://www.scopus.com/inward/record.url?scp=84894490917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894490917&partnerID=8YFLogxK
U2 - 10.5772/57225
DO - 10.5772/57225
M3 - Article
AN - SCOPUS:84894490917
VL - 11
JO - International Journal of Advanced Robotic Systems
JF - International Journal of Advanced Robotic Systems
SN - 1729-8806
IS - 1
M1 - 4
ER -