A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
What's wrong with mean-squared error?
Digital images and human vision
Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
Information Retrieval
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Cognates can improve statistical translation models
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Determining Map Quality through an Image Similarity Metric
RoboCup 2008: Robot Soccer World Cup XII
Evaluation of maps using fixed shapes: the fiducial map metric
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
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The maps generated by robots in real environment are usually incomplete, distorted, and noisy. The map quality is a quantitative performance measure of a robot's understanding of its environment. Map quality also helps researcher study the effects of different mapping algorithms and hardware components used. In this paper we present an algorithm to assess the quality of the map generated by the robot in terms of a ground truth map. To do that, First, localized features are calculated on the pre-evaluated map. Second, nearest neighbor of each valid local feature is searched between the map and the ground truth map. The quality of the map is defined according to the number of the features having the correspondence in the ground truth map. Three feature detectors are tested in terms of their effectiveness, these are the Harris corner detector, Hough Transform and Scale Invariant Feature Transform.