Semi-direct Sparse Odometry with Depth Prior

Visual odometry can be divided into direct and indirect methods. Indirect methods which have more computation complexities are more robust, while direct methods are faster but less robust. To achieve the goal in terms of efficiency and robustness, we propose a novel semi-direct visual odometry approach with depth prior, which can be applied to most monocular direct odometry approaches. The proposed method is implemented based Direct Sparse Odometry. Experimental results on TUM datasets show that the proposed approach, which combines the advantages of both the direct and indirect methods, are more efficient and robust than the state-of-the-art method.