Visual SLAM Research On Sewage Pipeline Robot

Sewage pipes are widely distributed,which is closely related to our daily life.Once the sewage pipes are blocked,leaked or etc,the soil,groundwater and people's life and property will be in danger.It is of great significance to detect sewage pipes timely and effectively for natural environment and personal property.A robot is used as a carrier for infrared,laser radar,gyroscopes,cameras or other sensors to the inspection of sewage pipes instead of manual work.Recently,the mileage positioning method,accelerometer location method,weld location method and GPS positioning method are used to the detection of sewage pipelines,but these algorithms generally cost a lot of time and has low positioning accuracy.In order to detect and analyze the internal environment of the sewage pipe in real time,this paper adopts one of the key technologies of artificial intelligence named Visual Simultaneous Localization And Mapping(VSLAM)to solve the problem of sewage pipeline robot self localization and mapping.The main work of this paper includes the followings:1.Compare the current state-of-art LSD-SLAM and ORB-SLAM algorithms with the combination of theoretical analysis and experiment.The advantages and disadvantages of the Overall frameworks,the data extraction methods and the positioning accuracy are analyzed in detail.2.Put forward the application of the semi dense ORB-SLAM algorithm according to the environmental characteristics of the sewage pipe.In order to improve the speed and accuracy of the images' high gradient pixels in the construction of maps,the reduced sampling of the captured key frame images is carried out.For monocular image polar line search,bilinear interpolation method is applied to improve the pixel accuracy of the sub pixel on the polar line,and then accurately calculate the pixel value of the sub pixel on the pole line.Since the sewage pipeline and the pipeline environment texture is similar and uneven illumination,in order to improve the matching accuracy between pipeline images,reduce the effect of illumination changes on localization and map building,the mean normalized cross-correlation method(NCC)is used to solve these problems and to improve the accuracy of map building.The empirical parameter values involved in the algorithm are designed,and the optimum parameter values are obtained.Finally compared with the original semi dense ORB-SLAM2 algorithm using standard data sets,the ORB-SLAM algorithm of sewage pipes improves the location accuracy by 6.9%,and the map of the semi dense map constructed is clearer.3.By using the pioneer 3DX robot as the carrier and the notebook camera as the real data source of the algorithm,the ORB-SLAM algorithm is transplanted into robot ROS system platform.In order to complete the hardware and software implementation of the algorithm,the dark,semi-closed corridor environment is used to simulate the sewage pipeline environment and carry out the experiment.The experimental results show that the algorithm can be well adapted to the sewage pipe environment,and has a certain application potential and value.