基于激光雷达与IMU融合的农业机器人定位方法OACSTPCD

[Objective]High-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots.However,the traditional global navigation satellite system(GNSS)localization method faces numer-ous limitations,such as tree shadow,electromagnetic interference,and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology.To address the deficiencies and achieve precise localization of agricultural ro-bots independent of GNSS,a localization method was proposed based on the fusion of three-dimensional light detection and ranging(LiDAR)data and inertial measurement unit(IMU)information to enhance localization accuracy and reliability. [Methods]LiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching.By integrating real-time motion parameter measurements from the IMU with LiDAR data,a high-precision localization so-lution for agricultural robots was achieved through a specific fusion algorithm.Firstly,the LiDAR-obtained point cloud data was pre-processed and the depth map was used to save the data.This approach could reduce the dimensionality of the original LiDAR point cloud,and eliminate the disorder of the original LiDAR point cloud arrangement,facilitating traversal and clustering through graph search.Given the presence of numerous distinct crops like trees in the agricultural environment,an angle-based clustering method was adopted.Specific angle-based clustering criteria were set to group the point cloud data,leading to the segmentation of different clus-ters of points,and obvious crops in the agricultural environment was effectively perceived.Furthermore,to improve the accuracy and stability of positioning,an improved three-dimensional normal distribution transform(3D-NDT)localization algorithm was proposed.This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization.Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data,a point cloud clustering operation was used in place of down sampling operation,thereby improving matching accuracy and positioning precision.Secondly,to address potential constraints and shortcomings of using a single sensor for robot localization,a multi-sensor information fusion strategy was deployed to improve the localization accuracy.Specifically,the extended Kalman filter algorithm(EKF)was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information.The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultur-al robot,and by combining with the LiDAR-derived localization information,the localization of the agricultural robot could be more accurately estimated.This fusion approach maximized the advantages of different sensors,compensated for their individual limita-tions,and improved the overall localization accuracy of the agricultural robot. [Results and Discussions]A series of experimental results in the Gazebo simulation environment of the robot operating system(ROS)and real operation scenarios showed that the fusion localization method proposed had significant advantages.In the simulation envi-ronment,the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm,respective-ly,while in the experimental scenario,these errors were 3.3 and 3.3 cm,respectively,which were significantly better than the tradition-al 3D-NDT localization algorithm.These findings showed that the localization method proposed in this study could achieve high-pre-cision localization in the complex agricultural environment,and provide reliable localization assistance for the autonomous function-ing of agricultural robots. [Conclusions]The proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localiza-tion solution for the autonomous operation of agricultural robots in areas with limited GNSS reception.Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms,the localization accuracy of agri-cultural robots could be significantly improved,which could provide a new reference for the intelligence and automation of agricultur-al production.