Abstract
This paper proposes a point feature-based outdoor SLAM method using only omnidirectional LIDAR. 3D local occupancy grid mapping and ground plane classification are conducted as a pre-process to refine the point cloud. Then uncertain objects are clustered with Euclidean distance. For applications in rural environments, point features are utilized because clusters are extracted from unclear and overlapped objects. To improve matching performance, the similarity of clusters is calculated with a Hausdorff distance and correspondence filtering with the point histogram is implemented. With the correspondence filtering, we can reduce false matches that cannot be removed from the initial matcher and thus improve the SLAM accuracy. The remaining point features are used as landmarks in SLAM, and the effectiveness of the scheme is verified through simulations with the real-world dataset.
| Original language | English |
|---|---|
| Title of host publication | 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 218-223 |
| Number of pages | 6 |
| ISBN (Print) | 9781467379700 |
| DOIs | |
| Publication status | Published - 2015 Dec 16 |
| Event | 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015 - Goyang City, Korea, Republic of Duration: 2015 Oct 28 → 2015 Oct 30 |
Other
| Other | 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015 |
|---|---|
| Country | Korea, Republic of |
| City | Goyang City |
| Period | 15/10/28 → 15/10/30 |
Keywords
- 3D Harris corner
- Hausdorff distance
- point clouds
- SLAM
- Velodyne
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
Cite this
Point feature-based outdoor SLAM for rural environments with geometric analysis. / Kim, Dong Il; Chae, Heewon; Song, Jae-Bok; Min, Jihong.
2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 218-223 7358940.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Point feature-based outdoor SLAM for rural environments with geometric analysis
AU - Kim, Dong Il
AU - Chae, Heewon
AU - Song, Jae-Bok
AU - Min, Jihong
PY - 2015/12/16
Y1 - 2015/12/16
N2 - This paper proposes a point feature-based outdoor SLAM method using only omnidirectional LIDAR. 3D local occupancy grid mapping and ground plane classification are conducted as a pre-process to refine the point cloud. Then uncertain objects are clustered with Euclidean distance. For applications in rural environments, point features are utilized because clusters are extracted from unclear and overlapped objects. To improve matching performance, the similarity of clusters is calculated with a Hausdorff distance and correspondence filtering with the point histogram is implemented. With the correspondence filtering, we can reduce false matches that cannot be removed from the initial matcher and thus improve the SLAM accuracy. The remaining point features are used as landmarks in SLAM, and the effectiveness of the scheme is verified through simulations with the real-world dataset.
AB - This paper proposes a point feature-based outdoor SLAM method using only omnidirectional LIDAR. 3D local occupancy grid mapping and ground plane classification are conducted as a pre-process to refine the point cloud. Then uncertain objects are clustered with Euclidean distance. For applications in rural environments, point features are utilized because clusters are extracted from unclear and overlapped objects. To improve matching performance, the similarity of clusters is calculated with a Hausdorff distance and correspondence filtering with the point histogram is implemented. With the correspondence filtering, we can reduce false matches that cannot be removed from the initial matcher and thus improve the SLAM accuracy. The remaining point features are used as landmarks in SLAM, and the effectiveness of the scheme is verified through simulations with the real-world dataset.
KW - 3D Harris corner
KW - Hausdorff distance
KW - point clouds
KW - SLAM
KW - Velodyne
UR - http://www.scopus.com/inward/record.url?scp=84962689657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962689657&partnerID=8YFLogxK
U2 - 10.1109/URAI.2015.7358940
DO - 10.1109/URAI.2015.7358940
M3 - Conference contribution
SN - 9781467379700
SP - 218
EP - 223
BT - 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -