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基于ORB-SLAM2的温室移动机器人定位研究

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针对温室内道路环境复杂,且温室移动机器人无法使用GNSS进行定位的问题,本文开展了基于ORB-SLAM2的温室定位研究.首先,对Realsense D455型深度相机获取的温室彩色图像和深度信息进行预处理,通过图像金字塔和灰度质心法实现ORB特征的尺度和旋转不变性,完成精确有效的特征点匹配;其次,利用跟踪线程参考关键帧跟踪、匀速模型跟踪和重定位跟踪进行粗定位,再使用局部地图跟踪进行精定位,实现对相机位姿的精确求解;再次,结合局部建图线程,在完成关键帧插入、近期地图点筛选、新地图点筛选、新地图点重建、局部BA优化和局部关键帧筛选的基础上,应用共视图方法建立地图点;最后,结合闭环线程,通过候选回环、计算相似变换、回环融合和位姿图优化对全图地图进行回环修正,从而实现温室内的实时定位与建图.选取辣椒生长初期、中期和成熟期3种不同作物生长期的温室进行实机测试,算法生成的轨迹与实际轨迹基本契合,X轴的均方根误差分别为0.686 2、0.355 0、0.492 5 m,平均绝对误差分别为0.588 3、0.2937、0.455 4 m,Z 轴的均方根误差分别为 0.1497、0.0718、0.368 6 m,平均绝对误差分别为 0.0986、0.046 4、0.282 5 m.试验结果表明该方法可为温室移动机器人的定位与导航提供技术支撑.
Greenhouse Mobile Robot Localization Based on ORB-SLAM2
Aiming at the complex road environment in greenhouse and the problem that greenhouse mobile robots cannot use GNSS for localization,research and experiments on greenhouse localization were carried out based on ORB-SLAM2.Firstly,the color image and depth information of greenhouse acquired by the depth camera Realsense D455 were preprocessed,and the scale and rotation invariance of ORB features was achieved by the image pyramid and grayscale center-of-mass method to complete accurate and effective feature point matching.Secondly,coarse localization was done by using tracking thread reference key frame tracking,homogeneous model tracking,and repositioning tracking,and then fine localization was done by using local map tracking to achieve an accurate solution for the camera pose.Thirdly,combining with the local map building thread,applying the common-view method to build up the map points based on the completion of the key frame insertion,the recent map point screening,the new map point screening,the new map point reconstruction,the local BA optimization,and the local key frame screening.Finally,combined with the closed-loop thread,the full map was corrected by loopback correction through the candidate loopback,computation of similarity transformation,loopback fusion,and position map optimization,so as to realize the greenhouse in the real-time localization and map building.Three greenhouses with different crop growth conditions in the early,middle and maturity stages of pepper growth were selected for real-machine testing,and the trajectories generated by the algorithm basically matched the actual trajectories,with the root-mean-square errors on the X-axis of 0.686 2 m,0.355 0 m,0.492 5 m,and the average absolute errors of 0.588 3 m,0.293 7 m,and 0.455 4 m,respectively,and on the Z-axis of 0.149 7 m,0.071 8 m,0.368 6 m,and the average absolute errors of 0.098 6 m,0.046 4 m,and 0.282 5 m,respectively.The experimental results showed that the method could provide technical support for the localization and navigation of greenhouse mobile robots.

greenhousesrobotmappinglocalizationdepth cameraORB-SLAM2

李旭、阳奥凯、刘青、伍硕祥、刘大为、邬备、谢方平

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湖南农业大学机电工程学院,长沙 410128

湖南省智能农机装备重点实验室,长沙 410128

农业农村部南方智能育秧(苗)重点实验室,益阳 413055

湖南省智能育秧育苗装备工程技术研究中心,益阳 413055

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温室 机器人 建图 定位 深度相机 ORB-SLAM2

2024

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCD北大核心
影响因子:1.904
ISSN:1000-1298
年,卷(期):2024.55(z1)