Kalman filtering with real-time applications
Kalman filtering with real-time applications
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic monocular machine vision
Machine Vision and Applications
Applications of dynamic monocular machine vision
Machine Vision and Applications
Computer vision research at INRIA
International Journal of Computer Vision
A vision sensor for building 3-D models of structured environment
Selected papers from the 9th Scandinavian conference on Image analysis : theory and applications of image analysis II: theory and applications of image analysis II
CyPhone—bringing augmented reality to next generation mobile phones
DARE '00 Proceedings of DARE 2000 on Designing augmented reality environments
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In this paper, we address the problem of the recovery of motion and a 3-D model of environment with moving monocular vision. The vision sensor developed here is able model structured environments in real time. Features, such as corners and straight lines, are tracked from image sequences. Their location and the motion of the camera are estimated with Kalman Filters. Unlike in the conventional solution, these estimations are separated. There is a Kalman filter for each feature of the model. With this separation, it is simple to insert new objects into the environment model as they appear in the camera view. In addition, it is possible to use several separate modelers that update the common environment model. It is shown with several tests that motion estimation works well even with rather difficult real world scenes, but to obtain better 3-D models, corners and straight lines are not discriminating enough to model such scenes.