Semi-direct Sparse Odometry with Robust and Accurate Pose Estimation for Dynamic Scenes

The localization accuracy and robustness of visual odometry systems for static scenes can be significantly degraded in complex real-world environments with moving objects. This paper addresses the problem by proposing a semi-direct sparse visual odometry (SDSO) method designed for dynamic scenes. With the aid of the pixel-level semantic information, the system can not only eliminate dynamic points but also construct more accurate photometric errors for subsequent optimization. To obtain an accurate and robust camera pose in dynamic scenes, we propose a dual error optimization strategy that minimizes the reprojection and photometric errors consecutively. The proposed method has been extensively evaluated on the public datasets like the TUM dynamic dataset and KITTI dataset. The results demonstrate the effectiveness of our method in terms of localization accuracy and robustness compared with both the original direct sparse odometry (DSO) method and state-of-the-art methods for dynamic scenes.