The environment perception and navigation ability of robots are the basis of robot interaction with environment. The scene understanding is the prerequisite of robot autonomous navigation and navigation is the ultimate goal of robot scene understanding, therefore, a mobile robot semantic map navigation system is designed. Firstly, a dense pointcloud map is constructed by using a deep convolutional neural network. Then, the mapping relationship between the depth camera data and the laser data is proposed, for purpose of realizing their interconversion. And a method of constructing two-dimensional raster map by simulating laser data by using the data of the depth camera is proposed. Finally, a method that semantic information is integrated into the two-dimensional grid map is proposed, realizing the semantic navigation of the service robot. The experimental results showed that compared with other robotic navigation systems, the proposed algorithm can better realize the interactions between service robots and people and the environment.
Due to the difference of scenes, the uncertainty of detection for line features in stereo visual odometer reduces the accuracy of initialization for line features, and the point-line fusion visual odometry is easy to cause uneven distribution of features in feature extraction and matching process. The problems, which will lead to incomplete feature extraction and thus inaccuracy of pose estimation. In order to solve the problems, we design a stereo visual odometry system called PLWM-VO, which adaptively allocates the weights of the point features and the endpoints of line features in each frame through the adaptive weight assignment model. Firstly, the improved SAD algorithm is used to accurately detect the endpoints of line features, through which improves the registration accuracy of line features’ endpoints in the left and right, and improves the accuracy of line features in the initialization process. Secondly, the adaptive weight assignment model based on region partition assigns weights to point features and endpoints of line features. In this process, the effect of uneven feature distribution on the accuracy of pose estimation is reduced by region growth. Finally, experiment results on the open dataset shows that the proposed algorithm exhibits higher precision and stability than the state-of-the-art methods, which improves the positioning accuracy of our designed stereo visual odometry system.