提出一种基于颜色直方图和SIFT混合特征的机器人视觉环境感知方法。该方法将颜色直方图的"色"与SIFT算法的"形"有机结合,有效提高了感知精度和实时性。对图像进行平均亮度调整并对颜色直方图特征加入主颜色直方图,使之对环境光照和动态变化具有更好的鲁棒性;通过控制特征点数和加入局部颜色统计信息方式改进SIFT算法,提高了特征生成速度和匹配准确度。利用分级匹配方法加速了特征检索过程,并采用本文提出的基于数据知识的推理方法进一步提高了感知精度。仿真与实验结果表明,随着数据库规模扩大,本文方法在感知精度和实时性上的性能优势越发明显。

An image-matching method for robot environmental perception based on hybrid features from color histograms based on the scale-invariant feature transform (SIFT) is proposed.The SIFT is combined with color histograms to make a compromise between high perception accuracy and real-time processing needs. First,images are processed by making an average of the lightness,then the extracted features are added to the main color histogram,which is more robust against lightness and dynamics in the environment.The number of SIFT values is controlled and local color statistical information is added to the SIFT,which is more accurate and faster for real-time matching.After wards,the process of features-retrieval is accelerated by hierarchical matching.Finally,the scheme is optimized using the proposed reasoning method based on previous knowledge from databases,to further improve the accuracy of perception the simulation and experiment results show that when the scale of the database is growing,the advantage of the proposea method proposed is prominent.