Name
Abstract | ||
|---|---|---|
In this paper, we propose a semantic feature extraction based on the light detection and ranging (LiDAR) sensor of an indoor driving robot and a location recognition method using the extracted features. After extracting semantic features based on the corner position and direction and shape of the corner for a wall or door in an indoor driving environment, and matching it with the corner information of the map, position recognition is performed using the collinearity method. It shows excellent performance with low computational complexity in embedded computers. We tested the proposed method in a real indoor environment using real robots and sensors. The performance of the location recognition system was verified by comparison with the widely used AMCL (Adaptive Monte Carlo Localization) algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/UR55393.2022.9826250 | 2022 19th International Conference on Ubiquitous Robots (UR) |
Keywords | DocType | ISBN |
Localization system,2d LiDAR,indoor robot,semantic feature extraction,light detection,indoor driving robot,location recognition method,corner position,indoor driving environment,corner information,position recognition,collinearity method,low computational complexity,indoor environment,sensors,location recognition system,widely used AMCL algorithm,Adaptive Monte Carlo Localization | Conference | 978-1-6654-8254-7 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sang-Hyeon Bae | 1 | 0 | 0.34 |
| Sung-Hyeon Joo | 2 | 0 | 0.34 |
| Jun-Hyun Choi | 3 | 0 | 0.34 |
| Hyunjin Park | 4 | 218 | 25.86 |
| Tae-Yong Kuc | 5 | 0 | 0.34 |