Abstract
Mobile robot localization is the task of estimating the robot pose in a given environment. Among many localization techniques, Monte Carlo localization (MCL) is known to be one of the most reliable methods for pose estimation of a mobile robot. However, as outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose using MCL in outdoor environments. Therefore, this study proposes a novel approach, the Hausdorff distance-based matching method using the objects commonly observed from air and ground (COAG) features for outdoor MCL algorithm. The Hausdorff distance is exploited to measure the similarity between the COAG features extracted from the robot and the elevation map. The experimental results in real environments show that the success rate of outdoor MCL increases and the proposed method is useful for robust outdoor localization using an elevation map.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Control, Automation and Systems |
| Pages | 686-689 |
| Number of pages | 4 |
| Publication status | Published - 2011 Dec 1 |
| Event | 2011 11th International Conference on Control, Automation and Systems, ICCAS 2011 - Gyeonggi-do, Korea, Republic of Duration: 2011 Oct 26 → 2011 Oct 29 |
Other
| Other | 2011 11th International Conference on Control, Automation and Systems, ICCAS 2011 |
|---|---|
| Country | Korea, Republic of |
| City | Gyeonggi-do |
| Period | 11/10/26 → 11/10/29 |
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Keywords
- Hausdorff distance
- Mobile robots
- Monte Carlo localization
- Outdoor localization
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Outdoor mobile robot localization using Hausdorff distance-based matching between COAG features of elevation maps and laser range data. / Ji, Yong Hoon; Song, Jae-Bok; Choi, Ji Hoon.
International Conference on Control, Automation and Systems. 2011. p. 686-689 6106279.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Outdoor mobile robot localization using Hausdorff distance-based matching between COAG features of elevation maps and laser range data
AU - Ji, Yong Hoon
AU - Song, Jae-Bok
AU - Choi, Ji Hoon
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Mobile robot localization is the task of estimating the robot pose in a given environment. Among many localization techniques, Monte Carlo localization (MCL) is known to be one of the most reliable methods for pose estimation of a mobile robot. However, as outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose using MCL in outdoor environments. Therefore, this study proposes a novel approach, the Hausdorff distance-based matching method using the objects commonly observed from air and ground (COAG) features for outdoor MCL algorithm. The Hausdorff distance is exploited to measure the similarity between the COAG features extracted from the robot and the elevation map. The experimental results in real environments show that the success rate of outdoor MCL increases and the proposed method is useful for robust outdoor localization using an elevation map.
AB - Mobile robot localization is the task of estimating the robot pose in a given environment. Among many localization techniques, Monte Carlo localization (MCL) is known to be one of the most reliable methods for pose estimation of a mobile robot. However, as outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose using MCL in outdoor environments. Therefore, this study proposes a novel approach, the Hausdorff distance-based matching method using the objects commonly observed from air and ground (COAG) features for outdoor MCL algorithm. The Hausdorff distance is exploited to measure the similarity between the COAG features extracted from the robot and the elevation map. The experimental results in real environments show that the success rate of outdoor MCL increases and the proposed method is useful for robust outdoor localization using an elevation map.
KW - Hausdorff distance
KW - Mobile robots
KW - Monte Carlo localization
KW - Outdoor localization
UR - http://www.scopus.com/inward/record.url?scp=84863078492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863078492&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84863078492
SN - 9781457708350
SP - 686
EP - 689
BT - International Conference on Control, Automation and Systems
ER -