基于视觉SLAM的飞机模拟座舱三维地图构建

针对飞机座舱副驾驶的研究需要,为使得机械臂能够在飞机座舱完成导航任务,旨在构建一个可用于机械臂导航的飞机模拟座舱三维地图。 针对特征点分布情况对即时定位与地图构建( simultaneous localization and mapping,简称 SLAM)的建图精度的影响,通过实验对比,验证了 ORB-SLAM 改进的 ORB( oriented FAST and rotated BRIEF)特征检测算法相对于 OpenCV 库中 SIFT,SURF 和 ORB 算法检测提取的特征点分布更加均匀,更适用于 SLAM。 采用 Kinect V2. 0 作为深度信息图像和彩色图像的数据采集设备,在飞机模拟座舱真实的环境下,结合 ROS 系统和 ORBSLAM2 系统框架,构建了飞机座舱内的三维稠密点云地图。 针对点云地图存在数据大和难以实现导航等问题,采用了 OctoMap 数据模型,该数据模型能够压缩点云,调节分辨率和判断空间是否被占据,将点云地图转化为八叉树地图,最终获得数据小和适用于导航的三维八叉树地图。

Construction of 3D map of aircraft simulated cockpit based on visual SLAM

According to the research needs of the co-pilot of the aircraft cockpit, in order to enable the robotic arm to complete the navigation task in the aircraft cockpit, this paper aims to construct a 3D map of the simulated cockpit of the aircraft that can be used for the robotic arm navigation. In view of the influence of the distribution of feature points on the accuracy of SLAM( simultaneous localization and mapping) mapping, through experimental comparison, this paper verifies that the improved ORB( oriented FAST and rotated BRIEF) feature detection algorithm of ORB-SLAM is more uniform than the feature points detected and extracted by the SIFT, SURF and ORB algorithms in the OpenCV library, which is more suitable for SLAM. In this paper, Kinect V2. 0 is used as the data acquisition device for depth information images and color images. In the real environment of the simulated cockpit of the aircraft, combined with the ROS system and the ORBSLAM2 system framework, a 3D dense point cloud map in the aircraft cockpit is constructed. In view of the problems of large data and difficult to achieve navigation in point cloud maps, the OctoMap data model is used, which can compress point clouds, adjust resolution and determine whether the space is occupied, convert the point cloud map into an octree map, and finally obtain a 3D octree map with small data and suitable for navigation.