Robust Robot Pose Estimation for Challenging Scenes With an RGB-D Camera

Rigid robot pose estimation with an RGB-D camera has attracted substantial research attention recently for the reason that the RGB-D camera can capture RGB and depth information simultaneously. Despite the huge progress that has been made, there are still some unresolved issues like the pose estimation in texture-less or structure-less scenes. Aiming at this problem, this paper presents a robust real-time pose estimation method with an RGB-D camera for texture-less and structure-less scenes. Our contributions are threefold. First, we present an improved ORB algorithm for extracting reliable inliers, in which adaptive threshold setting method of FAST corners decision is proposed for extracting sufficient keypoints. In addition, an effective inliers refinement method, based on motion smoothness consistency constraint, is introduced for obtaining fine inliers. Second, based on the characteristics of RGB-D camera, this paper proposes a novel hybrid reprojection errors optimization model (HREOM) to estimate pose by concurrently minimizing 3D-3D and 3D-2D reprojection errors. Third, we carry out comprehensive experiments on TUM public datasets to demonstrate the robustness, accuracy, and real-time of the proposed system. The quantitative evaluations show that our system can extract sufficient inliers in those extreme scenes. Furthermore, our method performs as good as or better than other state-of-the-art solutions. Notably, our system can operate in the texture-less and structure-less environment, while other methods are prone to failure.