TY - GEN
T1 - Visual SLAM in indoor environments using autonomous detection and registration of objects
AU - Lee, Yong Ju
AU - Song, Jae Bok
PY - 2009
Y1 - 2009
N2 - For successful SLAM, landmarks for pose estimation should be continuously observed. This paper proposes autonomous detection of objects as visual landmarks for visual SLAM. Primitive features such as color and intensity, SIFT keypoints, and contour information are integrated to investigate environmental images and to distinguish objects from the background. Autonomous object detection can enable a robot to extract some objects without any prior information and it can help a vision system to cope with unknown environments. In addition, an adaptive weighting scheme and the use of a gradient of the gray scale are proposed to improve the performance of the proposed scheme. Using detected objects as landmarks, a robot can estimate its pose. A grid map of an unknown environment is built using an IR scanner and the detected objects are mapped in the grid map, which results in a hybrid grid/vision map. Visual SLAM using objects can have the less number of landmarks than other visual SLAM schemes using corners and lines. Various experiments show that the algorithm proposed in this paper can improve visual SLAM of a mobile robot.
AB - For successful SLAM, landmarks for pose estimation should be continuously observed. This paper proposes autonomous detection of objects as visual landmarks for visual SLAM. Primitive features such as color and intensity, SIFT keypoints, and contour information are integrated to investigate environmental images and to distinguish objects from the background. Autonomous object detection can enable a robot to extract some objects without any prior information and it can help a vision system to cope with unknown environments. In addition, an adaptive weighting scheme and the use of a gradient of the gray scale are proposed to improve the performance of the proposed scheme. Using detected objects as landmarks, a robot can estimate its pose. A grid map of an unknown environment is built using an IR scanner and the detected objects are mapped in the grid map, which results in a hybrid grid/vision map. Visual SLAM using objects can have the less number of landmarks than other visual SLAM schemes using corners and lines. Various experiments show that the algorithm proposed in this paper can improve visual SLAM of a mobile robot.
KW - Object recognition
KW - SIFT
KW - SLAM
KW - Visual attention
UR - http://www.scopus.com/inward/record.url?scp=78651538351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78651538351&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89859-7_21
DO - 10.1007/978-3-540-89859-7_21
M3 - Conference contribution
AN - SCOPUS:78651538351
SN - 9783540898580
T3 - Lecture Notes in Electrical Engineering
SP - 301
EP - 314
BT - Multisensor Fusion and Integration for Intelligent Systems
T2 - 7th IEEE International Conference on Multi-Sensor Integration and Fusion, IEEE MFI 2008
Y2 - 20 August 2008 through 22 August 2008
ER -