【摘要】 针对单目视觉机器人定位问题,提出一种基于改进的尺度不变特征变换(SIFT)的Monte Carlo自定位方法.应用改进的SIFT方法提取特征,既能保证对图像光强变化、尺度缩放、三维视角和噪声具有不变性,又能减少SIFT算法产生的特征点及其抽取和匹配的时间.在机器人移动过程中,环境特征点的观测信息和里程计信息通过粒子滤波相融合,获得了更准确的环境标志点坐标.仿真实验结果验证了该方法的有效性.更多还原

【Abstract】 To deal with the localization problem of robot equipped with monocular camera,a Monte Carlo method based on scale invariant feature transform (SIFT) is proposed. The features are extracted by modified SIFT to make the features invariant to changes in illumination,scale,3D viewpoint and noise,and to reduce the number of features generated by SIFT as well as their extraction and matching time. During robot motion,the information from feature observations is fused with that from the odometry by particle filter,so more exact coordinates of the features are gotten. Experimental results show the effectiveness of the approach.更多还原