论文标题
室内动态场景的RGB-D大满贯算法
A RGB-D SLAM Algorithm for Indoor Dynamic Scene
论文作者
论文摘要
视觉大满贯技术是机器人独立探索未知环境的关键技术之一。基于视觉传感器的相机姿势的准确估计是自主导航和定位的基础。但是，大多数视觉大满贯算法基于静态环境假设，无法在动态环境中估算准确的相机姿势。为了解决此问题，提出了一种用于室内动态环境的视觉大满贯算法。首先，根据RGB-D相机的深度信息消除了一些移动对象，并通过优化亮度和深度误差来获得初始摄像头姿势，然后进一步消除了移动对象。并且，初始静态背景再次用于姿势估计。经过几次迭代后，获得了更准确的静态背景和更准确的相机姿势。实验结果表明，与以前的研究结果相比，在低动态室内场景和高动态室内场景中，提出的算法均可在较高的姿势估计精度中获得更高的估计精度。
Visual slam technology is one of the key technologies for robot to explore unknown environment independently. Accurate estimation of camera pose based on visual sensor is the basis of autonomous navigation and positioning. However, most visual slam algorithms are based on static environment assumption and cannot estimate accurate camera pose in dynamic environment. In order to solve this problem, a visual SLAM algorithm for indoor dynamic environment is proposed. Firstly, some moving objects are eliminated based on the depth information of RGB-D camera, and the initial camera pose is obtained by optimizing the luminosity and depth errors, then the moving objects are further eliminated. and, the initial static background is used for pose estimation again. After several iterations, the more accurate static background and more accurate camera pose is obtained. Experimental results show that, compared with previous research results, the proposed algorithm can achieve higher pose estimation accuracy in both low dynamic indoor scenes and high dynamic indoor scenes.
