Abstract
Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene. Nevertheless, this process normally relies on feature-matching techniques, which is a drawback for combining sensors that are not able to deliver common features. The combination of ToF and RGB cameras is an instance that problem. Typically, the fusion of these sensors is based on the extrinsic parameter computation of the coordinate transformation between the two cameras. This leads to a loss of colour information because of the low resolution of the ToF camera, and sophisticated algorithms are required to minimize this issue. This work proposes a method for sensor registration with non-common features and that avoids the loss of colour information. The depth information is used as a virtual feature for estimating a depth-dependent homography lookup table (Hlut). The homographies are computed within sets of ground control points of 104 images. Since the distance from the control points to the ToF camera are known, the working distance of each element on the Hlut is estimated. Finally, two series of experimental tests have been carried out in order to validate the capabilities of the proposed method.
Highlights
Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene
In this work a monocular setup is adopted. The fusion of these two systems is addressed by computing the extrinsic parameters for the homogenous transformation between them, which means that the method efficiency relies on the cameras calibration and the accuracy of the depth measurements of the ToF camera
Two series of experimental tests were conducted to evaluate the image registration procedure proposed in this work
Summary
Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene. The fusion of these sensors is based on the extrinsic parameter computation of the coordinate transformation between the two cameras This leads to a loss of colour information because of the low resolution of the ToF camera, and sophisticated algorithms are required to minimize this issue. The fusion of these two systems is addressed by computing the extrinsic parameters for the homogenous transformation between them, which means that the method efficiency relies on the cameras calibration and the accuracy of the depth measurements of the ToF camera. Very interesting results are presented in [11], where the authors calibrate the system within a range of 400 mm and use the depth values as an additional observed variable in the global approximation function In this case, the method was tested in real scenes and numerical results report mean errors within 2–3 pixels. Regardless, in this configuration the most important drawbacks of the stereo system, which are the occlusions and textureless regions, remain unsolved
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