Zhang Xiaoyu, Wang Wei, Qi Xianyu, Liao Ziwei, Wei Ran
Robotics Institute, Beihang University, Beijing 100191, China.
Beijing Evolver Robotics Technology Co., Ltd., Beijing 100192, China.
Sensors (Basel). 2019 Sep 2;19(17):3795. doi: 10.3390/s19173795.
Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.
同步定位与地图构建（SLAM）是各类应用中的一个基本问题。对于室内环境而言，平面是主要特征，受测量噪声的影响较小。在本文中，我们提出了一种使用RGB-D相机的新型点平面SLAM系统。首先，我们从RGB图像中提取特征点，从深度图像中提取平面。然后，可以利用平面的轮廓在全局地图中找到平面对应关系。考虑到实际平面的尺寸有限，我们利用平面边缘的约束。一般来说，平面边缘是两个相互垂直平面的交线。因此，我们不是基于直线约束，而是从边缘线计算并生成假设的垂直平面，从而得到更多的平面观测值和约束，以减少估计误差。为了利用室内环境中的正交结构，我们还添加了平面的结构（平行或垂直）约束。最后，我们使用所有这些特征构建一个因子图。通过最小化代价函数来估计相机位姿和全局地图。我们在公开的RGB-D基准测试中对我们提出的系统进行了测试，与其他先进的SLAM系统相比，展示了其稳健且准确的位姿估计结果。
