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3D Reconstruction of Scale-Invariant Features for Mobile Robot localization


Dong-Fan Shen, Jong-Shill Lee, Se-Kee Kil, Je-Goon Ryu, Eung-Hyuk Lee, Seung-Hong Hong


Vol. 6  No. 3  pp. 101-109


A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two images' feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it was easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two cameras and capture the 3 frames per second. The experimental results show ±6cm maximum error in the range of less than 2m and ±15cm maximum error in the range of between 2m and 4m.


DoG, SIFT, 3D reconstruction, Localization