基于视觉的四旋翼无人机自主定位与控制系统  

An Autonomous Vision-based Localization and Control System for Quadrotor UAV

作　　者：曹美会 鲜斌[1] 张旭[1] 文曦 

机构地区：[1]天津大学电气与自动化工程学院,天津大学机器人与自主系统研究所,天津市过程检测与控制重点实验室,天津300072 [2]航天神舟飞行器有限公司,天津300301

出　　处：《信息与控制》2015年第2期190-196,202,共8页Information and Control

基　　金：天津市应用基础与前沿技术研究计划重点项目(14JCZDJC31900)

摘　　要：针对GPS(global positioning system)信号缺失环境下无人机(UAV)自主飞行控制问题,设计了一种基于视觉的自主定位与控制方法.首先通过增加特征点提取数量和优化关键帧存储来对传统视觉SLAM(simultaneous localization and mapping)算法进行改进,提高了算法的鲁棒性与通用性.其次,引入光流传感器作为视觉SLAM地图丢失情况下辅助位置信息测量单元,提高无人机飞行控制的安全性,并成功地克服视觉SLAM图像丢失问题和光流法存在的位置漂移问题.然后采用EKF(extended Kalman filter)融合无人机位置和3维加速度信息,得到了较为精确的位置信息,同时提高了信号输出频率.最后,利用上述方法获取的无人机位置信息设计PID(proportion integration differentiation)和RISE(robust integral of the signum of the error)非线性控制器,增加了算法的鲁棒性.为验证该控制策略的有效性,搭建了四旋翼无人机视觉控制系统实验平台.该平台采用嵌入式控制系统架构,使用机载计算机运行所提算法,避免了图像及控制命令在无线传输过程中引起的时间延迟和信号干扰.室外飞行实验表明,此控制方案实现了自主定位与飞行控制功能.A autonomous vision-based localization and control method has been developed for unmanned aerial vehi-cles （UAV）operating in GPS-denied environments.To improve the robustness of the vision-based localiza-tion scheme,the visual simultaneous localization and mapping （SLAM）algorithm is enhanced by increasing its number of features and by optimizing its storage of key frames.The map-losing issue is avoided by combi-ning the optical flow algorithm with the original visual SLAM algorithm.By merging the visual SLAM and op-tical flow algorithms together,short-term precise velocity and long-term drift-free position estimations are a-chieved simultaneously.To obtain more accurate and faster states estimation,a state-of-the-art EKF algorithm is utilized to fuse the position information within the onboard IMU readings.Based on the fused localization information,a PID and nonlinear robust RISE controller is designed to increase the robustness of the flight controller.The proposed localization and control algorithms are implemented on a self-built quadrotor UAV testbed.To avoid time delays and signal interference from the wireless transmission process,the motion states are estimated through an onboard embedded computer.Outdoor flight experimental results demonstrate that the proposed strategies achieve good autonomous control performance.

关 键 词：四旋翼无人机 视觉同步定位与建图(SLAM) 扩展卡尔曼滤波(E KF ) 光流传感器 自主控制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]