基于激光雷达与IMU融合的农业机器人定位方法
[目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提，而常用的全球卫星导航系统（Global Navigation Satellite System，GNSS）定位方法在农业环境中容易受到树木遮挡、电磁干扰等因素影响，因而，提出一种基于三维激光雷达（Light Detection and Ranging，LiDAR）与惯性测量单元（Inertial Measurement Unit，IMU）信息融合的农业机器人定位方法。[方法]首先，利用基于角度的聚类方法对激光雷达点云数据进行信息处理，并与三维正态分布变换（3D Normal Distribution Transform...
Saved in:
| Published in | 智慧农业（中英文） Vol. 6; no. 3; pp. 94 - 106 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国农业科学院农业信息研究所
30.05.2024
西南大学 工程技术学院,重庆 400715,中国%中国汽车工程研究院股份有限公司,重庆 401122,中国%重庆文理学院智能制造工程学院,重庆 402160,中国 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-8094 |
| DOI | 10.12133/j.smartag.SA202401009 |
Cover
| Abstract | [目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提，而常用的全球卫星导航系统（Global Navigation Satellite System，GNSS）定位方法在农业环境中容易受到树木遮挡、电磁干扰等因素影响，因而，提出一种基于三维激光雷达（Light Detection and Ranging，LiDAR）与惯性测量单元（Inertial Measurement Unit，IMU）信息融合的农业机器人定位方法。[方法]首先，利用基于角度的聚类方法对激光雷达点云数据进行信息处理，并与三维正态分布变换（3D Normal Distribution Transform，3D-NDT）定位算法相结合，在先验点云地图信息基础上实现基于激光雷达的实时定位；其次，为了克服单传感器定位方法的局限性，利用扩展卡尔曼滤波（Extended Kalman Filter，EKF）算法对激光雷达定位信息与IMU里程计信息进行融合，进一步提升农业机器人的定位精度。最后，分别在机器人操作系统（Robot Operating System，ROS）的Gazebo仿真环境中，以及真实作业场景中进行实验，验证提出的定位算法的有效性。[结果和讨论]融合定位方法在仿真环境中的纵向和横向平均定位误差分别为1.7和1.8 cm，而在实验中的纵向和横向平均定位误差分别为3.3和3.3 cm，均小于传统3D-NDT定位算法的定位误差。[结论]提出的融合定位方法能够满足农业机器人在弱GNSS环境下自主作业的定位要求，为农业机器人提供了一种新的定位方法。 |
|---|---|
| AbstractList | [目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提，而常用的全球卫星导航系统（Global Navigation Satellite System，GNSS）定位方法在农业环境中容易受到树木遮挡、电磁干扰等因素影响，因而，提出一种基于三维激光雷达（Light Detection and Ranging，LiDAR）与惯性测量单元（Inertial Measurement Unit，IMU）信息融合的农业机器人定位方法。[方法]首先，利用基于角度的聚类方法对激光雷达点云数据进行信息处理，并与三维正态分布变换（3D Normal Distribution Transform，3D-NDT）定位算法相结合，在先验点云地图信息基础上实现基于激光雷达的实时定位；其次，为了克服单传感器定位方法的局限性，利用扩展卡尔曼滤波（Extended Kalman Filter，EKF）算法对激光雷达定位信息与IMU里程计信息进行融合，进一步提升农业机器人的定位精度。最后，分别在机器人操作系统（Robot Operating System，ROS）的Gazebo仿真环境中，以及真实作业场景中进行实验，验证提出的定位算法的有效性。[结果和讨论]融合定位方法在仿真环境中的纵向和横向平均定位误差分别为1.7和1.8 cm，而在实验中的纵向和横向平均定位误差分别为3.3和3.3 cm，均小于传统3D-NDT定位算法的定位误差。[结论]提出的融合定位方法能够满足农业机器人在弱GNSS环境下自主作业的定位要求，为农业机器人提供了一种新的定位方法。 TP242%S126; [目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提,而常用的全球卫星导航系统(Global Navigation Satellite System,GNSS)定位方法在农业环境中容易受到树木遮挡、电磁干扰等因素影响,因而,提出一种基于三维激光雷达(Light Detection and Ranging,LiDAR)与惯性测量单元(Inertial Measure-ment Unit,IMU)信息融合的农业机器人定位方法.[方法]首先,利用基于角度的聚类方法对激光雷达点云数据进行信息处理,并与三维正态分布变换(3D Normal Distribution Transform,3D-NDT)定位算法相结合,在先验点云地图信息基础上实现基于激光雷达的实时定位;其次,为了克服单传感器定位方法的局限性,利用扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法对激光雷达定位信息与IMU里程计信息进行融合,进一步提升农业机器人的定位精度.最后,分别在机器人操作系统(Robot Operating System,ROS)的Gazebo仿真环境中,以及真实作业场景中进行实验,验证提出的定位算法的有效性.[结果和讨论]融合定位方法在仿真环境中的纵向和横向平均定位误差分别为1.7和1.8 cm,而在实验中的纵向和横向平均定位误差分别为3.3和3.3 cm,均小于传统3D-NDT定位算法的定位误差.[结论]提出的融合定位方法能够满足农业机器人在弱GNSS环境下自主作业的定位要求,为农业机器人提供了一种新的定位方法. |
| Abstract_FL | [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. |
| Author | 刘洋 冀杰 潘登 赵立军 李明生 |
| AuthorAffiliation | 西南大学 工程技术学院,重庆 400715,中国%中国汽车工程研究院股份有限公司,重庆 401122,中国%重庆文理学院智能制造工程学院,重庆 402160,中国 |
| AuthorAffiliation_xml | – name: 西南大学 工程技术学院,重庆 400715,中国%中国汽车工程研究院股份有限公司,重庆 401122,中国%重庆文理学院智能制造工程学院,重庆 402160,中国 |
| Author_FL | LIU Yang LI Mingsheng PAN Deng JI Jie ZHAO Lijun |
| Author_FL_xml | – sequence: 1 fullname: LIU Yang – sequence: 2 fullname: JI Jie – sequence: 3 fullname: PAN Deng – sequence: 4 fullname: ZHAO Lijun – sequence: 5 fullname: LI Mingsheng |
| Author_xml | – sequence: 1 fullname: 刘洋 organization: 西南大学工程技术学院,重庆400715,中国 – sequence: 2 fullname: 冀杰 organization: 西南大学工程技术学院,重庆400715,中国 – sequence: 3 fullname: 潘登 organization: 中国汽车工程研究院股份有限公司,重庆401122,中国 – sequence: 4 fullname: 赵立军 organization: 重庆文理学院智能制造工程学院,重庆402160,中国 – sequence: 5 fullname: 李明生 organization: 西南大学工程技术学院,重庆400715,中国 |
| BookMark | eNotzzFLAzEABeAMFay1f8FBcLwzyaVNAi6lqC1UHbRIuxy5S65SNIVGEZ0cVHRocVHwLLo6uThYD6x_5u6s_8LSOr3l4z3eAsjojlYALCFoI4wcZ7VtmyPRPRYte7eEISYQQcgzIIshL1oMcjIP8sa0IYSYI4xpIQvWkpcojvrp90Vydfv79DEejeJhv7pVHz_3krubn_AyuR7EwzAdRMnjaxxFyVsYf_XSh8_0_X4RzAXi0Kj8f-ZAfWN9r1yxajub1XKpZvkIU2ZREkifC66YElRKn3CvKJDHiKdU4FCquPQCJoUkRFHfZwWHMYUJhwHzPDwhObAy6z0VOhC65bY7J109WXTPD_TZ9KkDIZu45ZnzhTGuNka6zcp2o9nYn5oZ-gNhFm1k |
| ClassificationCodes | TP242%S126 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | NSCOK 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.12133/j.smartag.SA202401009 |
| DatabaseName | 国家哲学社会科学文献中心 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitle_FL | Localization Method for Agricultural Robots Based on Fusion of LiDAR and IMU |
| EndPage | 106 |
| ExternalDocumentID | zhny202403008 ZHNYZYW2024003008 |
| GroupedDBID | ABDBF ACUHS ALMA_UNASSIGNED_HOLDINGS EDH GROUPED_DOAJ NSCOK TCJ TGD U1G U5N 2B. 4A8 92I 93N PSX |
| ID | FETCH-LOGICAL-c1278-74fdc9a9e8ea7ddc49b6a1b84beef377e9dbf8dad44e7cc85388e2490f8bb2ef3 |
| ISSN | 2096-8094 |
| IngestDate | Thu May 29 04:06:09 EDT 2025 Tue Jan 21 20:55:09 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | 扩展卡尔曼滤波 激光雷达定位 传感器融合 点云匹配 农业机器人 agricultural robots sensors fusion LiDAR localization extended Kalman filter point cloud matching |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c1278-74fdc9a9e8ea7ddc49b6a1b84beef377e9dbf8dad44e7cc85388e2490f8bb2ef3 |
| OpenAccessLink | http://dx.doi.org/10.12133/j.smartag.SA202401009 |
| PageCount | 13 |
| ParticipantIDs | wanfang_journals_zhny202403008 cass_nssd_ZHNYZYW2024003008 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-30 |
| PublicationDateYYYYMMDD | 2024-05-30 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationTitle | 智慧农业（中英文） |
| PublicationTitle_FL | Smart Agriculture |
| PublicationYear | 2024 |
| Publisher | 中国农业科学院农业信息研究所 西南大学 工程技术学院,重庆 400715,中国%中国汽车工程研究院股份有限公司,重庆 401122,中国%重庆文理学院智能制造工程学院,重庆 402160,中国 |
| Publisher_xml | – name: 中国农业科学院农业信息研究所 – name: 西南大学 工程技术学院,重庆 400715,中国%中国汽车工程研究院股份有限公司,重庆 401122,中国%重庆文理学院智能制造工程学院,重庆 402160,中国 |
| SSID | ssj0002912275 ssib057733647 ssib051372935 ssib042363126 |
| Score | 2.3854203 |
| Snippet | [目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提，而常用的全球卫星导航系统（Global Navigation Satellite System，GNSS）定位方法在农业环境... TP242%S126; [目的/意义]精准可靠的定位技术是智能农业机器人开展自主导航作业的重要前提,而常用的全球卫星导航系统(Global Navigation Satellite System,GNSS)定位方法在... |
| SourceID | wanfang cass |
| SourceType | Aggregation Database |
| StartPage | 94 |
| Title | 基于激光雷达与IMU融合的农业机器人定位方法 |
| URI | https://www.ncpssd.cn/Literature/articleinfo?id=ZHNYZYW2024003008&type=journalArticle https://d.wanfangdata.com.cn/periodical/zhny202403008 |
| Volume | 6 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journal Collection issn: 2096-8094 databaseCode: DOA dateStart: 20190101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0002912275 providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate issn: 2096-8094 databaseCode: ABDBF dateStart: 20210901 customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn isFulltext: true dateEnd: 99991231 titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn omitProxy: true ssIdentifier: ssj0002912275 providerName: EBSCOhost – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources issn: 2096-8094 databaseCode: M~E dateStart: 20200101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: true ssIdentifier: ssib057733647 providerName: ISSN International Centre |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV29b9QwFI-qsrAgECBKoeqAp-pK4nzZEouTy6kgtQutaLucLl_t0kPi2oFODIBgaMUCEqWClYmFgXIS5Z-5O8qfwMZ7L7lLSlNRkKLIZz8__977-ZxnJ3E07ZYdhYmwkqTGzRhvM8ZpLeSWW4vtkIskMm3TxReF5xecuSXr3rK9PDb-q_TU0tZmOBttV75X8j-sQh7wim_J_gOzI6WQAWngF87AMJzPxDELbCYbzFMssPAsAhY4zGvg4wtQJOCQLJBMesxzWSCYF-CBwgKE784vYaYMmKiTKp0JwQKXSVBlkQaHST-Xh8yAfmJzICyZEnm7WY4KSAZy6qQQhAGMJEgmyz5yOYyDqVRSRQdxKreqOTDNJ0iUo-qIVoAtRq5cuEOZ0XIGqYHagpqFQNk7VuKQa6BynXl6UeIgZpnZDqhKdcBnNh5QojxsPIfplZdLuEV3-vVRB8d6ykYmUBxUk3nKyu0ES5QzgymgRWXKyTJ0hhpC9JlqjKSJRnA5h_8EpsH0jDSk3ZnB787j_h1-yVc28V5n3K7IRJMNSgg8YwMn0UBCZ9KiBLilBAKJ4EwZpHmoUPrD_iaxL2U9UPmUaCCAU6BDMM5Ph_5nhRL3LnVZp8JJRfcCoCbho36R2QAOVnqVwWd1NjccvRJxcV3lMGuHwCz7tvYwCHBKY51ZuqDnUlloaNDmGCejDm7gbREIOzobMBa01mbvK-x6uqHrsoizRk-_bq-3H1O5SdsUnOMQkOil1SC4FMIkxzGNYmZkG3h7vZjZ2C5uY5qvBGDQySVQRduKj8zL90NAcLcroUEUGsEMnV5XbKet9lopsl68qF3Ip8TTKhvfLmlj2-uXtTv9D91ed3fw_Un_2cuf774cHR72DnZhxDp6v9N_9eLH3tP-8_3ewd5gv9t_-7HX7fY_7fW-7QzefB18fn1FW2oEi_5cLf_SSy0yuIsbGqdxJFsyEUnLjePIkqHTMkJhhUmSmq6byDhMRdyKLStxowimGEIk3JJ6KsKQg8hVbbz9sJ1c06YdN41iE5dZ9BRf4pVpLIwolElkWwaE4xPaJBrdbHc6cXN1bmFldeUBeYTYmNCmcl8083G-0zxG1_W_CUxq54th54Y2vvloK7kJ85bNcIoY_g2EQuQJ |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E6%BF%80%E5%85%89%E9%9B%B7%E8%BE%BE%E4%B8%8EIMU%E8%9E%8D%E5%90%88%E7%9A%84%E5%86%9C%E4%B8%9A%E6%9C%BA%E5%99%A8%E4%BA%BA%E5%AE%9A%E4%BD%8D%E6%96%B9%E6%B3%95&rft.jtitle=%E6%99%BA%E6%85%A7%E5%86%9C%E4%B8%9A%EF%BC%88%E4%B8%AD%E8%8B%B1%E6%96%87%EF%BC%89&rft.au=%E5%88%98%E6%B4%8B&rft.au=%E5%86%80%E6%9D%B0&rft.au=%E6%BD%98%E7%99%BB&rft.au=%E8%B5%B5%E7%AB%8B%E5%86%9B&rft.date=2024-05-30&rft.pub=%E8%A5%BF%E5%8D%97%E5%A4%A7%E5%AD%A6+%E5%B7%A5%E7%A8%8B%E6%8A%80%E6%9C%AF%E5%AD%A6%E9%99%A2%2C%E9%87%8D%E5%BA%86+400715%2C%E4%B8%AD%E5%9B%BD%25%E4%B8%AD%E5%9B%BD%E6%B1%BD%E8%BD%A6%E5%B7%A5%E7%A8%8B%E7%A0%94%E7%A9%B6%E9%99%A2%E8%82%A1%E4%BB%BD%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8%2C%E9%87%8D%E5%BA%86+401122%2C%E4%B8%AD%E5%9B%BD%25%E9%87%8D%E5%BA%86%E6%96%87%E7%90%86%E5%AD%A6%E9%99%A2%E6%99%BA%E8%83%BD%E5%88%B6%E9%80%A0%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E9%87%8D%E5%BA%86+402160%2C%E4%B8%AD%E5%9B%BD&rft.issn=2096-8094&rft.volume=6&rft.issue=3&rft.spage=94&rft.epage=106&rft_id=info:doi/10.12133%2Fj.smartag.SA202401009&rft.externalDocID=zhny202403008 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fzhny%2Fzhny.jpg |