TY - GEN
T1 - Evaluation of features through grid association for building a sonar map
AU - Lee, Se Jin
AU - Cho, Dong Woo
AU - Chung, Wan Kyun
AU - Lee, Yucheol
AU - Lim, Jong Hwan
AU - Kang, Chul Ung
AU - Yun, Won Soo
PY - 2006
Y1 - 2006
N2 - This paper addresses a new feature map building method that can minimizes the appearance of phantom features by using only sparse sonar data. The approach is composed of extraction of features and building a probability grid map using only the footprint of sparse sonar data, estimation of position uncertainty of the feature, and evaluation of the reliable features. A virtual circle association frame model has been developed, which associates two sonar footprints into a virtual circle frame. Using this model, the geometric primitives such as lines, points, and arc features are separately extracted. While extracting the features, a grid map is also built using the orientation probability approach. The position uncertainty of each extracted feature is, then, estimated by considering both the position uncertainty of the robot and the measurement uncertainty of the sonar sensor. Finally, the reliable features among all extracted ones are evaluated from grid association method. The proposed methods have been tested in a real home environment with a mobile robot.
AB - This paper addresses a new feature map building method that can minimizes the appearance of phantom features by using only sparse sonar data. The approach is composed of extraction of features and building a probability grid map using only the footprint of sparse sonar data, estimation of position uncertainty of the feature, and evaluation of the reliable features. A virtual circle association frame model has been developed, which associates two sonar footprints into a virtual circle frame. Using this model, the geometric primitives such as lines, points, and arc features are separately extracted. While extracting the features, a grid map is also built using the orientation probability approach. The position uncertainty of each extracted feature is, then, estimated by considering both the position uncertainty of the robot and the measurement uncertainty of the sonar sensor. Finally, the reliable features among all extracted ones are evaluated from grid association method. The proposed methods have been tested in a real home environment with a mobile robot.
KW - Feature mapping
KW - Feature position uncertainty
KW - Grid map
KW - Orientation probability
KW - Sparse sonar data
UR - http://www.scopus.com/inward/record.url?scp=33845600479&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2006.1642096
DO - 10.1109/ROBOT.2006.1642096
M3 - Conference contribution
AN - SCOPUS:33845600479
SN - 0780395069
SN - 9780780395060
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2615
EP - 2620
BT - Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006
T2 - 2006 IEEE International Conference on Robotics and Automation, ICRA 2006
Y2 - 15 May 2006 through 19 May 2006
ER -