A robust dense 3D feature map is built only with monocular vision and odometry. Monocular vision mounted on the robot front-end tracks the 3D natural landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature matching pairs. SIFT features are highly distinctive and invariant to image scaling, rotation, and change in 3D viewpoints. A fast SIFT feature matching algorithm is implemented with the KD-Tree based nearest search approach in the time cost of O(log2N), and matches with large error are eliminated by epipolar line restriction. A map building algorithm based on 3D spatial SIFT landmarks is designed and implemented. Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.