基于精简路标的机器人视觉归航算法

A Robot Visual Homing Algorithm Based on Reduced Landmarks

  • 摘要: 在受生物导航方式启发的局部视觉归航算法中,ALV(average landmark vector)算法因其模型简单、归航性能较好以及所需存储空间小等优点受到了广泛的重视.在非结构化环境中,ALV算法常常需要使用图像的局部特征作为自然路标点,在这种情况下,路标的对应性问题难以保证,同时在保证归航性能的前提下如何合理地精简路标数量也尚无有效的解决方法.针对上述问题,对基于双曲面镜的折反射全景图像进行了研究,提出了horizon环域的概念.在环域内提取SIFT(scale invariant feature transform)特征作为自然路标点并结合ALV模型提出了一种改进的基于自然路标的ALV算法.改进算法有效地缩小了路标点的提取区域,较好地保证了路标点的对应性并精简了路标点的数量.多个实际场景的实验表明,这种算法有效提高了归航精度.

     

    Abstract: Among all the local visual homing algorithms inspired by the navigation of living creatures, ALV (average landmark vector) algorithm gains extensive attention for its simple model, better homing performance and less storage space, etc. In unstructured environments, ALV algorithm often uses local features of the image as natural landmarks. Under this circumstance, it is hard to ensure the correspondence of landmarks, at the same time, on the premise of ensuring its homing performance, there is no effective way to reduce the number of landmarks reasonably. In order to solve these problems, the concept of horizon ring-shaped region is put forward by the study on catadioptric panoramic image based on hyperboloidal mirror. An improved ALV algorithm based on natural landmarks is proposed by combining natural landmarks which are SIFT (scale invariant feature transform) features extracted from the ring-shaped region with ALV model. The improved algorithm effectively narrows the area for extracting landmarks and ensures the correspondence of landmarks, at the same time, it reduces the number of landmarks. Many experiments in different actual scenes indicate that the proposed algorithm improves homing precision effectively.

     

/

返回文章
返回