基于多传感器融合的无人机自主避障方法  

Autonomous obstacle avoidance method for UAV based on multi-sensor fusion

作　　者：张红蕾 盛志超 叶林 杨强强 方勇[1] ZHANG Honglei;SHENG Zhichao;YE Lin;YANG Qiangqiang;FANG Yong(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China;Shanghai Enjoy Move technology Co.,Ltd.,Shanghai 201306,China)

机构地区：[1]上海大学通信与信息工程学院,上海200444 [2]上海映驰科技有限公司,上海201306

出　　处：《激光杂志》2024年第1期229-235,共7页Laser Journal

基　　金：国家自然科学基金(No.61901254)。

摘　　要：针对无人机利用单一传感器进行避障时存在准确度低、信息缺失等问题,提出了一种基于多传感器融合的无人机自主避障方法。通过改进的贝叶斯融合算法将二维激光雷达与深度相机获取的点云信息进行融合,以弥补二维激光雷达无法检测的区域。同时,利用融合点云生成八叉树地图,并根据不断更新的地图信息对无人机进行实时航迹重规划,实现无人机在未知环境中的自主避障。实验结果表明,所研究方法不仅提高了无人机感知周围环境的准确度,融合点云的均方根误差小于0.06 m,还具有良好的避障效果,无人机与障碍物的距离均大于0.5 m,保证了其在未知环境中的安全飞行。Aiming at the problems of low accuracy and information loss using single sensor for obstacle avoidance in UAVs(Unmanned Aerial Vehicles),a UAV autonomous obstacle avoidance method based on multi-sensor fusion was proposed in this paper.The improved Bayesian fusion algorithm is used to fuse the point cloud acquired by 2D lidar and depth camera to compensate for the areas that the 2D lidar cannot detect.At the same time,an octree map is generated based on the fused point cloud,and the UAV is replanned in real-time according to the updated map information to achieve autonomous obstacle avoidance in unknown environments.The experimental results show that the proposed method not only improves the accuracy of UAV perception of the surrounding environment,with the root mean square error of the fused point cloud is less than 0.06 m,but also has good obstacle avoidance performance,with the distance between the UAV and obstacles is greater than 0.5 m,ensuring its safe flight in unknown environments.

关 键 词：无人机 二维激光雷达 深度相机 自主避障 

分 类 号：TN911[电子电信—通信与信息系统]