Ultra-precision micro-structured surfaces are becoming increasingly important in a range of application areas, including engineering optics, biological products, metrology artifacts, data storage, etc. However, there is a lack of surface characterization methods for the micro-structured surfaces with sub-nanometer accuracy. Although some research studies have been conducted on 3D surface characterization, most of them are on freeform surfaces, which are difficult to be applied on the micro-structured surfaces because of their limited characterization accuracy and the repeated surface feature patterns in the micro-structured surfaces. In this paper, an automatic form error evaluation method (AFEEM) is presented to characterize the form accuracy of the micro-structured surfaces. The machined micro-structured surface can be measured by any 3D high resolution measurement instrument. The measurement data are converted and pre-processed for the AFEEM, which mainly consists of a coarse registration and a fine registration process. The coarse registration estimates an initial position of the measured surface for the fine registration by extracting the most perceptually salient points in the surfaces, computing the integral volume descriptor for each salient point, searching for the best triplet-point correspondence and calculating the coarse registration matrix. The fine registration aligns the measured surface to the designed surface by a proposed adaptive iterative closest point algorithm to guarantee sub-nanometer accuracy for surface characterization. A series of computer simulations and experimental studies were conducted to verify the AFEEM. Results demonstrate the accuracy and effectiveness of the AFEEM for characterizing the micro-structured surfaces.
In this paper we present a novel model-based hybrid technique for user localization and drift-free tracking in urban environments. In outdoor augmented reality, instantaneous 6-DoF user localization is achieved with position and orientation sensors such as GPS and gyroscopes. Initial pose obtained with these sensors is dominated by large positional errors due to coarse granularity of GPS data. Subsequent tracking is also erroneous as gyroscopes are prone to drifts and often need recalibration. We propose to use model-to-image registration technique to refine initial rough estimate for accurate user localization. Large positional errors in user localization are mitigated by aligning silhouettes of the model with that of the camera image using shape context descriptors as they are invariant to translation, scale and rotational errors. Once initialized, drift-free tracking is achieved by combining frame-to-frame and model-to-frame feature tracking. Frame-to-frame tracking is done by matching corners whereas edges are used for model-to-frame silhouette tracking. Final camera pose is obtained with M-estimators.
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