基于深度学习门牌检测的机器人室内定位方法

Deep Learning-Based Doorplate Detection for Mobile Robot Localization in Indoor Environments

提出了一种基于深度学习的门牌检测方法,以实现室内环境中移动机器人的全局定位。具体步骤为:基于MobileNet-SSD算法对单目相机获取的图像进行门牌区域检测;提出一种改进的旋转投影方法用于倾斜图像校正;通过kNN(k-Nearest Neighbors)算法进行门牌号识别;根据事先采集的各个门牌的正视模板图片进行SURF(Speeded Up Robust Features)特征点匹配,进而实现基于n点透视(PnP)问题的相机位姿求解;根据坐标变换实现移动机器人的全局定位。使用移动机器人在室内办公环境下进行定位实验,结果表明,基于该方法实现的平均位置误差约为7 cm,朝向误差为0.1712 rad,相较于只使用自适应蒙特卡罗方法时位置误差减小了约50%,朝向误差减小了约57%。

Herein,a deep learning-based doorplate detection method was proposed to realize the global positioning of mobile robots in indoor environments.First,the target detection algorithm based on MobileNet-SSD was used to detect the doorplate area of the image acquired using a monocular camera.Second,an improved rotating projection method was proposed to correct oblique images.Third,doorplate number recognition was performed using the knearest neighbor algorithm.Fourth,the speeded-up robust feature point matching was performed based on the precollected front-view template pictures of each doorplate.Then,the camera pose solution was achieved based on the PnP problem.Finally,the global positioning of the mobile robot was realized according to the coordinate transformation.Experiments using a mobile robot in an office environment show that the average position error of the mobile robot based on the proposed method is about 7 cm.Moreover,the orientation error is 0.1712 rad,which is reduced by about 50%compared with using only the adaptive Monte Carlo method,and the angle error is reduced by about 57%.