A fast and accurate algorithm is presented to register scans from an RGB-D camera, which rotates and scans the entire scene in an automated fashion on a pan-tilt platform. The proposed algorithm, Axis Bound Registration, exploits the movement of the camera that is bound by the two rotation axes of pan-tilt servos, so as to realize fast and accurate registration of acquired point clouds. The rotation parameters, including the rotation axes, pan-tilt transformations and the servo control mechanism, are calibrated beforehand. Subsequently, fast global registration can be performed during online operation with transformation matrices formed by the calibrated rotation axes and angles. In local registration, features are extracted and matched between two scenes. For robust registration, false-positive correspondences are rejected based on the distances of pre-oriented keypoint pairs, namely the circle of deviation constraint. Then, a more accurate registration can be achieved by minimizing the residual distances between correspondence pairs, while estimated transformations are bound to the rotation axes. Results of comparative experiments validate that the proposed method outperforms state-of-the-art algorithms of various approaches based on camera calibration, global registration, and simultaneous-localization-and-mapping in terms of root-mean-square error and computation time.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence