Simultaneous localization and mapping (SLAM) is one of the most important components in robot navigation. A novel SLAM algorithm based on monocular vision is proposed to overcome the difficulties in outdoor applications and quantitative analysis with traditional methods. Firstly, a key frame selection method is proposed to reduce the computational cost. Then the three-dimensional (3-D) structure of the environment and the positions of the camcorder are estimated based on matched feature points and the intrinsic parameters of the camcorder. A simple method with reasonable optimizing effect and computing cost is applied to get position and orientation of the camcorder. Finally, an adaptive bundle adjustment is adopted to optimize the 3D structure of the environment and the positions of the camcorder simultaneously. Digital elevation map (DEM) which is more suitable for robot navigation is also obtained. Quantitative and qualitative experimental results show that the loop closure error is less than 4%. The algorithm can reconstruct the environment and localize the camcorder accurately in nearly real time.