| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 102 === With development of low-cost RGB-D sensor which can capture high resolutiondepth and visual information synchronously, and the success of two-dimensionalsimultaneous localization and mapping, three-dimension surface reconstruction of environment has been a popular research. Most of the 3D environment reconstruction approaches rely on data registration. By doing so, three-dimensional dataset scanned in different viewpoints can be transformed into the same coordinate system by aligning overlapping components of these sets. The constructed 3D map can be used in robot vision, virtual and augmented reality, and entertainment. With providing both color and spatial information, humans or robots can easily perceive their environments, such as minimal invasive surgery. In general, the task of three-dimension environment reconstruction can be divided into three stages: feature descriptor estimation, outlier rejection, and transformation estimation. First, feature descriptor estimation is used to find some distinct features with their special characteristics. Second, feature outlier removal can remove the incorrect corresponding pairs between two consecutive frames. Third, transformation estimation uses the correct corresponding pairs to find the transformation matrix which can transfer different viewpoint frames into global coordinate.
In this thesis, the proposed method uses structure-entropy based feature to describe the energy in the environment. The region of the spatial structural change can be extracted because of their structure entropy energy. Then, a new method to remove outlier is presented, which is called entropy image matching. With finding maximum entropy energy of the overlapping area, the relative pose between two consecutive frames can be estimated roughly, which can serve as a good initial guess for transformation estimation. In the final step, transformation estimation uses the remaining region to implement iterative closest point (ICP) to determine a rigid transformation matrix. With that transformation matrix, all of the frames can be transformed into global coordinate and plot a 3D virtual map with point cloud format.
Experimental results demonstrate two dataset, Networked Control Systems laboratory (NCSLab) dataset and Autonomous Systems Lab (ASL) dataset repository: Apartment. The experimental results show that the accuracy of the proposed method is better than traditional ICP algorithm. And the proposed method is unaffected by color or even in complete darkness compared to RGB-based feature mapping method. A 3D mapping system that can generate 3D maps of indoor environments with spatial information features is presented.
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