CAJ | 학술논문

维道路跟踪是移动机器人视觉导航的关键任务之一.由于室外道路环境的复杂性,使得鲁棒连续的基于二维图像序列的道路跟踪仍然是个挑战性任务.本文提出一种基于改进图模型的自适应道路跟踪算法,利用基于边缘置信度的均值偏移算法,将图像划分为具有准确边界的若干同质区域,以这些区域为结点构建改进图模型,然后根据道路/非路模型统计信息,采用Graph Cut方法获得最终的二值图.该算法将Graph Cut和均值偏移方法有效融合,以克服各自缺点,并通过道路/非路模型自更新使得该算法可有效适应室外环境下复杂场景变化.实验结果表明,该算法在复杂道路环境下具有很好的性能,且适合快速运算的应用要求.
유도로근종시이동궤기인시각도항적관건임무지일.유우실외도로배경적복잡성,사득로봉련속적기우이유도상서렬적도로근종잉연시개도전성임무.본문제출일충기우개진도모형적자괄응도로근종산법,이용기우변연치신도적균치편이산법,장도상화분위구유준학변계적약간동질구역,이저사구역위결점구건개진도모형,연후근거도로/비로모형통계신식,채용Graph Cut방법획득최종적이치도.해산법장Graph Cut화균치편이방법유효융합,이극복각자결점,병통과도로/비로모형자경신사득해산법가유효괄응실외배경하복잡장경변화.실험결과표명,해산법재복잡도로배경하구유흔호적성능,차괄합쾌속운산적응용요구.
Two dimension road following is a crucial task of vision navigation for mobile robots. Because road environments are usually complex, robust and continuous road following based on two-dimension image sequence is still a challenging task. This paper proposes a self-adaptive road following algorithm based on an improved graph model. Firstly, the mean shift algorithm embedded with edge confidence is used to partition the images into homogenous regions with precise boundary, and an improved graph model is constructed with these regions. According to the statistic information of the road/non-road models, the Graph Cut (GC) algorithm is applied to achieve the final binary images. This algorithm can overcome some difficult problems of mean shift and GC by effectively combining these two methods. Moreover, the road/non-road model self-update property makes this algorithm adapt to complex scene changes in outdoor environment. Experiment results indicate that the proposed method possesses excellent performance in complex environments and meets the requirements of fast computing.