基于RGB-D图像的具有滤波处理和位姿优化的同时定位与建图

RGB-D相机能够同时获得彩色图像和深度图像，广泛用于同时定位与建图（SLAM）的研究.本研究针对RGB-D SLAM方法进行了两方面的改进: 一方面，改进点云滤波方法，从而更有效地去除RGB-D相机数据中的噪声和冗余; 另一方面，采用ICP算法提高相机位姿估计的精度，从而提高估计的相机运动轨迹的精度.在公开的数据集上对提出的RGB-D SLAM方法进行实验验证，结果表明，该方法能够有效提高移动机器人自主定位与建图的精度.
Abstract：RGB-D camera can capture color and depth images simultaneously, and is widely used for simultaneous localization and mapping (SLAM) research. In this article, The RGB-D SLAM method was improved from two aspects. Firstly, the point cloud filter method was improved to more effectively decrease the noise and redundancy of RGB-D camera data; secondly, an ICP algorithm was used to improve the estimated accuracy of the pose transformation matrix and the trajectories of camera movement. The proposed RGB-D SLAM method was verified on public datasets. The experimental results demonstrate that our RGB-D SLAM method can effectively improve the accuracy of the autonomous positioning and mapping of robots.