基于自适应阈值ORB特征提取的果园双目稠密地图构建

Construction of Binocular Dense Map of Orchard Based on Adaptive Threshold ORB Feature Extraction

针对果园阴暗光照条件下图像特征点匹配数量少、易丢失以及点云稀疏问题,对ORB-SLAM2算法进行了改进,提出了基于自适应阈值ORB特征点提取的果园双目三维地图稠密建图算法。首先在跟踪线程中提出一种自适应阈值的FAST角点提取方法,通过计算不同光照下图像平均像素求解阈值,对左右目图像提取ORB特征,增加了不同光照条件下的特征点匹配数量;然后根据特征点估计相机位姿完成局部地图跟踪,对跟踪线程产生的关键帧地图点进行BA优化完成局部地图构建。在原有算法基础上添加了基于ZED-stereo型相机双目深度融合的稠密建图模块,对左右目关键帧进行特征匹配获得图像对,利用图像对求解深度信息获取地图点,经过深度优化获取相机位姿,根据相机位姿进行局部点云的构建与拼接,最终对获得的点云地图进行全局BA优化,构建果园三维稠密地图。在KITTI数据集序列上进行测试,本文所改进的ORB-SLAM2算法的绝对轨迹误差更加收敛,轨迹误差标准差在00和07序列分别下降60.5%和62.6%,在其他序列上也有不同程度下降,表明本文算法定位精度较原始算法有所提高。不同光照环境下进行算法性能测试,结果表明本文算法较原始算法能更好地适应不同光照条件,在较强光照、正常光照、偏弱光照和阴雨天气下特征点平均匹配数量增加5.32%、4.53%、8.93%、12.91%。进行果园直线和稠密建图试验,结果表明直线行驶偏航角更加收敛,定位精确度高,关键帧提取数量较原始算法下降2.86%、平均跟踪时间减少39.3%;稠密建图效果好,能够很好地反映机器人位姿和果园真实环境信息,满足果园三维稠密点云地图构建需求,可为果园机器人导航路径规划提供支持。

To address the challenges of limited feature point matching,vulnerability to loss,and sparse point cloud in dark lighting conditions in orchards,the ORB-SLAM2 was improved by proposing an adaptive threshold-based algorithm for dense construction of binocular 3D orchard maps.Firstly,a FAST corner extraction method with adaptable threshold values was introduced in the tracking thread,and ORB features were extracted from left and right eye images by calculating the average pixel solution threshold across images captured under different lighting conditions,which effectively enhanced the number of feature point matches under different lighting conditions.Subsequently,local map tracking was performed based on camera pose estimation by using feature points and accomplished local map construction through bundle adjustment optimization of key frame map points derived from the tracking thread.Based on the original algorithm,a dense mapping module was incorporated by utilizing ZED-stereo binocular deep fusion to acquire image pairs through feature matching of key frames from the left and right eyes.Depth information was obtained by solving the image pairs,camera pose was determined via depth optimization,and local point clouds were constructed and stitched together based on the camera pose.Finally,global BA optimization was applied to refine the resulting point cloud map,enabling the construction of a three-dimensional dense map of an orchard.The improved ORB-SLAM2 algorithm demonstrated enhanced convergence in terms of absolute trajectory error when evaluated on the KITTI data set sequence.Specifically,the standard deviation of trajectory error was decreased by 60.5%and 62.6%in sequences 00 and 07,respectively,while also exhibiting varying degrees of improvement in other sequences.These results indicated a notable enhancement in positioning accuracy compared with the original algorithm.The results demonstrated that in comparison with the original algorithm,the proposed algorithm exhibited excellent adaptability to diverse lighting conditions.Specifically,it achieved an average increase of 5.32%,4.53%,8.93%and 12.91%in feature point matching under strong light,normal light,dark light,and rainy day respectively.The results demonstrated that the yaw angle exhibited enhanced convergence,resulting in higher positioning accuracy.Moreover,the proposed algorithm reduced the number of extracted key frames by 2.86%and decreased average tracking time by 39.3%compared with the original approach.Additionally,it achieved a favorable dense mapping effect,accurately reflecting both robot pose and real environmental information within the orchard.Consequently,this method satisfied the requirements for constructing a 3D dense point cloud map of an orchard and provided essential support for realizing navigation path planning for orchard robots.