A new computational approach to estimate the ego-motion of a camera from sets of point correspondences taken from a monocular image sequence is presented. The underlying theory is based on a decomposition of the complete set of model parameters into suitable subsets to be optimized separately; e.g.,
Depth estimation from a sequence of monocular images with known camera motion
✍ Scribed by Hanqi Zhuang; R. Sudhakar; Jen-yu Shieh
- Publisher
- Elsevier Science
- Year
- 1994
- Tongue
- English
- Weight
- 699 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0921-8890
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✦ Synopsis
This paper reports an approach of computing depth maps from a monocular image sequence, under the assumption that the camera motion is known. The direct depth estimation method is combined with the optical flow based method to improve estimation accuracy. The optical flow on and near moving edges arc computed using a corrclation tcclmiquc. The optical flow information is then fused with gradient information to estimate depth not only on the moving edges but also in the intcrnal regions. The depth estimation problem is formulated as a Kalman filter problem and is solved in three stages. In the prediction stage, the depth map estimated for thc current frame, together with knowledge of the camera motion, is uscd to predict the depth and depth variance at each pixel in the next frame. In Ihc estimation stage, a Kalman filter is employed to refine the predicted depth map. The resulting estimation algorithm takes into account the information from the neighboring pixels. In the smoothing stage, based on the error covariancc infl~rmation, morphological filtering is applied to reduce the cffcct of measurement noise and to fill in untrustablc areas. Simulation results are provided to demonstrate the cffcctivcness of the proposed method
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