CAJ | 학술논문

相对位姿估计是机器人视觉领域的研究热点。通过两帧数据来估计相机的六自由度位姿变换。充分挖掘TOF相机优势，提出了多个有效算法，用以保证估计精度。采用迭代最近点(ICP)算法估计位姿变换，为了克服ICP算法迭代发散问题，利用尺度不变特征点对估计初始值。为了提取有效特征点，根据统计学原理尺度化灰度图像，提高图像对比度。为了提高相机的测量精度，根据曝光时间越长，测量精度越高的原理，提出了融合多帧数据算法，使得融合后的数据帧中每个像素值均是在最长合理曝光时间下采集得到。同时提出了度量两个六自由度位姿变换差异的算法，并首次利用其跟踪ICP迭代过程。实验证明提出的算法可以有效估计相机六自由度位姿变换。
상대위자고계시궤기인시각영역적연구열점。통과량정수거래고계상궤적륙자유도위자변환。충분알굴TOF상궤우세，제출료다개유효산법，용이보증고계정도。채용질대최근점(ICP)산법고계위자변환，위료극복ICP산법질대발산문제，이용척도불변특정점대고계초시치。위료제취유효특정점，근거통계학원리척도화회도도상，제고도상대비도。위료제고상궤적측량정도，근거폭광시간월장，측량정도월고적원리，제출료융합다정수거산법，사득융합후적수거정중매개상소치균시재최장합리폭광시간하채집득도。동시제출료도량량개륙자유도위자변환차이적산법，병수차이용기근종ICP질대과정。실험증명제출적산법가이유효고계상궤륙자유도위자변환。
Relative pose estimation is a hot research topic in the community of robotic vision. 6 DOF pose transformation was estimated by two frames data. Several effective algorithms were proposed to guarantee the precision of the estimation which made full used of TOF camera. Iterative Closest Point (ICP) algorithm was used to estimate the pose transformation, in order to conquer the divergence problem of ICP, scaled Invariant Feature Transform (SIFT) feature pairs were employed to compute the initial value for ICP. The contrast of the image was increased for extracting the effective features by scaling the original gray image according to principle of statistics. Multiple frames were fused to improve the accracy of depth measurement based on the fact that the longer the exposure time was, the higher the accuracy was, and every pixels in the fused frame were captured with the longest valid exposure time. A methed for measuring the difference of two 6 DOF pose transformations was proposed, which was applied to track the iterations of ICP. The experiments have demonstrated the effectiveness of the algorithms proposed in this paper.