When the SLAM algorithm is used to provide positioning services for a robot in an indoor scene, dynamic obstacles can interfere with the robot’s observation. Observing the ceiling using an upward-looking camera that has a stable field of view can help the robot avoid the disturbance created by dynamic obstacles. Aiming at the indoor environment, we propose a new ceiling-view visual odometry method that introduces plane constraints as additional conditions. By exploiting the coplanar structural constraints of the features, our method achieves better accuracy and stability in a ceiling scene with repeated texture. Given a series of ceiling images, we first use the semi-direct method with the coplanar constraint to preliminarily calculate the relative pose between camera frames and then exploit the ceiling plane as an additional constraint. In this step, the photometric error and the geometric constraint are both optimized in a sliding window to further improve the trajectory accuracy. Due to the lack of datasets for ceiling scenes, we also present a dataset for the ceiling-view visual odometry for which the LiDAR-Inertial SLAM method provides the ground truth. Finally, through an actual scene test, we verify that, in the ceiling environment, our method performs better than the existing visual odometry approach.