Aiming at the obstacle detection problem caused by the loss of environmental information in sparse Simultaneous Localization And Mapping (SLAM) algorithm, an autonomous location and obstacle detection method of robot based on vision was proposed. Firstly, the parallax map of the observed scene was obtained by binocular camera. Secondly, under the framework of Robot Operating System (ROS), localization and mapping node and obstacle detection node were operated simultaneously. The localization and mapping node completed pose estimation and map building based on ORB-SLAM2. In the obstacle detection node, a depth threshold was introduced to binarize the parallax graph and the contour extraction algorithm was used to obtain the contour information of the obstacle and calculate the convex hull area of the obstacle, then an area threshold was introduced to eliminate the false detection areas, so as to accurately obtain the coordinates of obstacles in real time. Finally, the detected obstacle information was inserted into the sparse feature map of the environment. Experiment results show that this method can quickly detect obstacles in the environment while realizing autonomous localization of the robot, and the detection accuracy can ensure the robot to avoid obstacles smoothly.