基于光学传感器的多旋翼无人机同步定位与映射算法研究

Z. Ng, S. K. Phang
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引用次数: 2

摘要

无人驾驶飞行器(UAV)的进步已经渗透到我们生活的各个方面,从军事到家庭娱乐。自主无人机购买了一种新的感觉水平,使自主无人机的操作员只需在屏幕上投影的地图上选择位置就可以将无人机飞行到预定位置,这可以通过在无人机上实施GPS模块来完成。然而,轻微的干扰会对GPS信号造成巨大的影响,并且由于信号微弱,无法在室内操作自主无人机。因此,本研究论文的目标是开发一种替代无人机在封闭环境或树冠下不使用GPS模块的情况下检测其自身位置精度高达25cm的方法。它还将在低功耗处理单元上执行高精度和精确的同时定位和绘图(SLAM)算法,但对于无人机绘图和导航具有鲁棒性。为了进一步了解SLAM算法的工作原理,在地面计算机上进行了离线仿真。在此基础上,下载单眼脱机数据,并利用该数据进行单眼SLAM仿真。离线数据模拟完成后,在地面计算机上安装机器人操作系统(ROS),通过网络摄像头进行实时单目SLAM。从实时单目SLAM中,利用网络摄像头对图像进行捕捉,并对每张图像的特征点进行定位。这个过程将通过每个关键帧慢慢生成一个3D地图。然后利用无人机顶部的低功率处理单元执行实时SLAM,用于测绘和导航。简而言之,本研究论文的预期成果是为自主无人机的SLAM算法开发一种低功耗但鲁棒的处理单元,以确定其自身的位置,精度可达25cm,用于导航和测绘目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of simultaneous localization and mapping algorithm using optical sensor for multi-rotor UAV
The advancement of an autonomous Unmanned Aerial Vehicle (UAV) has permeated throughout various aspect of our lives, from military to home entertainment. Autonomous UAV has bought a new level of sensation which enables the operator of the autonomous UAV to fly the UAV to the predetermined location just by selecting the location on the map projected on the screen as this can be done by implementing the GPS module on the drone. However, a slight interference will cause a tremendous effect on the GPS signal and it is impossible to operate the autonomous UAV indoor due to the weak signal. Therefore, the objective of this research paper is to develop an alternative for UAV to detect its own location up to 25cm of accuracy without the use of GPS module in a closed environment or under the tree canopy. It is also to perform a highly accurate and precise Simultaneous Localization and Mapping (SLAM) algorithm on a low-power processing unit yet robust for UAV to map and navigate. In order to have further understand on how the SLAM algorithm works, the offline simulation was carried out on a ground computer. On top of that, a monocular offline data was downloaded and simulation of monocular SLAM was carried out with the data. Once the offline data simulation was completed, Robot Operating System (ROS) was then installed in the ground computer to perform real time monocular SLAM using a webcam. From the real time monocular SLAM, the webcam was used to capture the images and pinpoint the feature points of each image. This process will slowly generating a 3D map by each key frame. The real time SLAM was then performed with a low-powered processing unit on top of the UAV for mapping and navigation. In a nutshell, the expected outcome of this research paper is to develop a low-powered yet robust processing unit for SLAM algorithm in autonomous UAV to determine its own location up to the accuracy of 25cm for navigation and mapping purpose.
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