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.
翻译：视觉摄影机技术是机器人独立探索未知环境的关键技术之一。根据视觉传感器对照相机姿势进行精确估计是自主导航和定位的基础。然而,大多数视觉摄影机演算法都是基于静态环境假设,无法对动态环境中的摄影机姿势作出准确估计。为了解决这个问题,提出了室内动态环境的视觉SLAM算法。首先,根据RGB-D摄像头的深度信息,消除了一些移动物体,通过优化光度和深度误差获得初始照相机姿势,然后进一步消除移动物体。初步的静态背景再次用于配置估测。经过几次迭代,获得了更准确的静态背景和更准确的摄影机姿势。实验结果表明,与以往的研究结果相比,拟议的算法可以在低动态室内场和高动态室内场都实现更高的表面估计精度。
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