Title
Vision-based detection and pose estimation for formation of micro aerial vehicles
Abstract
This paper proposes an innovative method to detect micro aerial vehicles (MAVs) and estimate their relative pose in formation using a monocular on-board camera. Haar classifier is trained for autonomously detecting MAV in open scenes, like grasslands or obstruct-free playgrounds. In order to increase the robustness of the detection, a Kaiman filter has been employed to conduct image tracking. Contours of detected MAV have been extracted for shape matching. Point sets quantized from contours match with the given point sets using Hungarian algorithm and relaxation labeling based on shape contexts. Two techniques, affine and thin plate spline (TPS) transformation, are explored, while TPS is better in dealing with distorted shapes. In experiments, we develop and implement an innovative 2D shape-based pose estimation method by using only one monocular camera which results in fast and accurate performances.
Year
DOI
Venue
2014
10.1109/ICARCV.2014.7064533
ICARCV
Keywords
Field
DocType
tps transformation,microaerial vehicle formation,thin-plate spline transformation,kalman filter,kalman filters,monocular camera,image matching,shape matching,image tracking,distorted shapes,grasslands,contour matching,autonomous mav detection,monocular on-board camera,relaxation labeling,pose estimation,affine transformation,autonomous aerial vehicles,point set quantization,feature extraction,image classification,contour extraction,object tracking,cameras,2d shape-based pose estimation method,object detection,hungarian algorithm,obstruct-free playgrounds,open scenes,shape contexts,microrobots,haar transforms,affine transforms,haar classifier training,vision-based detection,robot vision,estimation,shape,labeling
Affine transformation,Hungarian algorithm,Computer vision,Object detection,Thin plate spline,Pattern recognition,Computer science,3D pose estimation,Robustness (computer science),Feature extraction,Pose,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2474-2953
1
0.35
References 
Authors
6
3
Name
Order
Citations
PageRank
Mengmi Zhang152.76
Feng Lin2101.31
Ben M. Chen3994131.58