College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China.
Sensors (Basel). 2019 Mar 1;19(5):1050. doi: 10.3390/s19051050.
Due to image noise, image blur, and inconsistency between depth data and color image, the accuracy and robustness of the pairwise spatial transformation computed by matching extracted features of detected key points in existing sparse Red Green Blue-Depth (RGB-D) Simultaneously Localization And Mapping (SLAM) algorithms are poor. Considering that most indoor environments follow the Manhattan World assumption and the Manhattan Frame can be used as a reference to compute the pairwise spatial transformation, a new RGB-D SLAM algorithm is proposed. It first performs the Manhattan Frame Estimation using the introduced concept of orientation relevance. Then the pairwise spatial transformation between two RGB-D frames is computed with the Manhattan Frame Estimation. Finally, the Manhattan Frame Estimation using orientation relevance is incorporated into the RGB-D SLAM to improve its performance. Experimental results show that the proposed RGB-D SLAM algorithm has definite improvements in accuracy, robustness, and runtime.
由于图像噪声、图像模糊以及深度数据与彩色图像之间的不一致性，现有稀疏红绿蓝-深度（RGB-D）同时定位与建图（SLAM）算法中通过匹配检测到的关键点的提取特征计算得到的成对空间变换的准确性和鲁棒性较差。考虑到大多数室内环境遵循曼哈顿世界假设，并且可以使用曼哈顿框架作为参考来计算成对的空间变换，提出了一种新的 RGB-D SLAM 算法。它首先使用引入的方向相关性概念进行曼哈顿框架估计。然后使用曼哈顿框架估计计算两个 RGB-D 帧之间的成对空间变换。最后，将使用方向相关性的曼哈顿框架估计纳入 RGB-D SLAM 中以提高其性能。实验结果表明，所提出的 RGB-D SLAM 算法在准确性、鲁棒性和运行时间方面都有一定的改进。
