Shuhang Yang;Yanqiu Xing;Tao Xing;Hangyu Deng;Zhilong Xi
{"title":"Multisensors Fusion SLAM-Aided Forest Plot Mapping With Backpack Dual-LiDAR System","authors":"Shuhang Yang;Yanqiu Xing;Tao Xing;Hangyu Deng;Zhilong Xi","doi":"10.1109/JSTARS.2024.3451175","DOIUrl":null,"url":null,"abstract":"The extraction of forest vertical structural parameters plays a crucial role in forest inventory. In recent years, light detection and ranging (LiDAR) has been widely applied in forest inventories due to its powerful 3-D reconstruction capabilities. The backpack laser scanning (BLS) is a lightweight LiDAR platform that significantly enhances the efficiency and accuracy of forest inventory. To address the issues of Global Navigation Satellite System (GNSS) signal occlusion and LiDAR scanning blind areas under the canopy, a multisensors fusion Simultaneous Localization and Mapping (SLAM) algorithm has been proposed, and a BLS device has been set up. The proposed SLAM algorithm fuses both horizontal and vertical LiDAR data by extracting the planar surface models. In addition, the proposed similar stem features are added to the feature point extraction in forest mapping. The accuracy of the results is validated through standing tree position, diameter at breast height (DBH) and tree height. When compared to other classic SLAM methods, the proposed method achieves 100% accuracy in standing tree extraction, reduces the error in DBH extraction by 85.56% (with an error of 2.05 cm), and decreases the error in tree height extraction by 83.44% (with an error of 0.79 m). The results show that the problem of poor GNSS under the canopy can be effectively addressed by the proposed SLAM algorithm in the study. Furthermore, multisensor data fusion and stem feature addition can provide more complete data support and more robust matching constraints, ultimately resulting in more accurate point cloud mapping.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666837","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10666837/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The extraction of forest vertical structural parameters plays a crucial role in forest inventory. In recent years, light detection and ranging (LiDAR) has been widely applied in forest inventories due to its powerful 3-D reconstruction capabilities. The backpack laser scanning (BLS) is a lightweight LiDAR platform that significantly enhances the efficiency and accuracy of forest inventory. To address the issues of Global Navigation Satellite System (GNSS) signal occlusion and LiDAR scanning blind areas under the canopy, a multisensors fusion Simultaneous Localization and Mapping (SLAM) algorithm has been proposed, and a BLS device has been set up. The proposed SLAM algorithm fuses both horizontal and vertical LiDAR data by extracting the planar surface models. In addition, the proposed similar stem features are added to the feature point extraction in forest mapping. The accuracy of the results is validated through standing tree position, diameter at breast height (DBH) and tree height. When compared to other classic SLAM methods, the proposed method achieves 100% accuracy in standing tree extraction, reduces the error in DBH extraction by 85.56% (with an error of 2.05 cm), and decreases the error in tree height extraction by 83.44% (with an error of 0.79 m). The results show that the problem of poor GNSS under the canopy can be effectively addressed by the proposed SLAM algorithm in the study. Furthermore, multisensor data fusion and stem feature addition can provide more complete data support and more robust matching constraints, ultimately resulting in more accurate point cloud mapping.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.