Abstract: Point features are mostly extracted in feature based visual simultaneous localization and mapping to estimate camera poses when a robot moves in an unfamiliar environment. However,camera trajectories cannot be estimated accurately if the environment information is not abundant. In this paper, a visual odometry was proposed based on point and line features for RGB-D camera in the environment lacking of feature points. Bundle adjustment (BA) is widely used in estimating camera poses and feature positions. An unavoidable problem of BA with Euclidean coordinates or inverse depth is ill convergence under certain conditions. So a solution was proposed, that integrates parallax bundle adjustment and BA with line features to minimize back-project error. Finally, the proposed approach was compared with other feature based simultaneous localization and mapping (SLAM) system on the dataset TUM. The experiment results show that the proposed approach improves the performance in real scenes lack of point features.