In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight back-end map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively improve the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.
翻译:在本文中,我们提出一个与RGB、深度、IMU和结构化平面信息结合的紧密组合的SLAM系统。传统稀疏点基于SLAM系统总是保持大量的地图点,以模拟环境。大量的地图点给我们带来了很高的计算复杂性,使得难以在移动设备上部署。另一方面,飞机是人造环境中的常见结构,特别是在室内环境中。我们通常可以使用少量飞机来代表大场景。因此,这篇文章的主要目的是降低稀疏点基于SLAM的高度复杂性。我们制作了一个轻量后端地图,由几架飞机和地图点组成,以同样或更精确的方式实现高效的捆绑调整。我们使用同质限制来消除许多平面点的参数,从而难以在移动设备上部署。另一方面,我们用同质和点对点的平面限制来区分参数和测量,并压缩测量部分以进一步提高BA的速度。我们还把飞机的精度信息纳入整个系统,以便实现稳健的平面特征提取、数据关联、数据组合以及全球一致的平面调整(BBBA)的精确度,最后,我们用一个精确的平面平面的平面结构进行我们使用一个数据分析方法进行一个精确的比重的比重的比重的系统。最后,我们用一个数据比重的比重的平平平平平平平平平平平面的平面的比。最后,我们用一个数据的系统,我们用一个数据的系统,我们用一个数据法进行一个精确的精确的平面的平面的平面的平面的平面的平面的平面的平面的比。我们用的方法进行一个比。