In the robot visual navigation task of the indoor environment, the detection of the drivable area is an indispensable part, which is the basis for ensuring the realization of the autonomous driving task. At present, many solutions are to detect the drivable area by identifying obstacles in the dataset, which lacks flexibility. Therefore, a drivable area detection method for indoor flat ground such as subway stations is proposed in this study to improve practicability. The classic MobileNetV3 network is applied to classify the collected front images and determine whether they are ground areas. Due to the influences of stickers such as landmarks and arrows on the indoor floor, it is necessary to further judge the non-ground area and distinguish it from conventional three-dimensional obstacles. In this study, the feature point matching between successive frames is adopted to obtain the camera moving distance, and the method of calculating the slope by straight line fitting is used to distinguish between three-dimensional obstacles and plane landmarks. Experiments show that the proposed method can better detect the drivable area in front of the robot and has high practical value.