一种基于BoW物体识别模型的视觉导航方法

针对复杂的室内环境,提出一种新的动态环境下的移动机器人视觉导航方法.该方法以室内常见物体作为自然路标,通过单目视觉建立识别模型来认知环境中的各种物体.首先对室内常见物体建立图像库,并对库中的大量图像采集SIFT特征;然后通过BoW模型来描述各幅图像,针对每类物体利用线性支持向量机(SVM)训练出物体识别模型;最后借助交互的手绘地图描述室内环境,移动机器人从中获得辅助路径以及自然路标的大概位置,从而完成导航任务.通过大量实验,从自然路标变化、目的区域变化、手绘地图偏差等多角度验证该方法的鲁棒性.实验结果表明,该导航方法操作简单高效,并具有人机交互性强、动态环境下适应能力高的优点.

Visual navigation method based on BoW object recognition model

A new visual navigation method for mobile robot to navigate in dynamic environment is proposed.This method relies on some natural landmarks,which are common objects in indoor environment.Robot can establish recognition model and cognize various objects in the environment by monocular camera.Firstly,an image database of indoor common objects is established,and the SIFT(scale invariant feature transform) features are extracted from these images.Then,each image in the database is described as a bag of words(BoW) model.The linear SVM(support vector machine) is used to discriminate every class of images.Finally,An interactive hand-drawn-route-map is used to describe the dynamic indoor environment,where robot can get auxiliary route and natural landmark location and navigate successfully.The robustness of this method was verified by experiments in different aspects including the natural landmark change,destination area change and hand-drawn map deviation.A lot of experiments in the indoor environment show that the proposed method is very simple and highly effective,and has the advantage of high man-machine interaction and high adaptability to the dynamic environment.