Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exp...Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.展开更多
Registration of TLS data is an important prerequisite to overcome the limitations of occlusion.Most existing registration methods rely on stems to determine the transformation parameters.However,the complexity of the ...Registration of TLS data is an important prerequisite to overcome the limitations of occlusion.Most existing registration methods rely on stems to determine the transformation parameters.However,the complexity of the registration problem increases dramatically as the number of stems grows.It is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two scans.This paper presents an automatic and fast registration of TLS point clouds in forest areas.It reduces stems by selecting from the overlap areas,which are recovered from the mode-based key points that are detected from crowns.The proposed method was tested in a managed forest in Finland,and was compared with the stem-based registration method without reducing stems.The experiments demonstrated that the mean rotation error was 2.09′,and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm,respectively.Compared with the stem-based method,the proposed method improves the registration efficiency significantly(818 s vs 96 s)and achieves similar results in terms of the mean registration errors(1.94′for rotation error,0.83 and 7.38 cm for horizontal and vertical translation error,respectively).展开更多
Light Detection and Ranging(LiDAR)sensors are popular in Simultaneous Localization and Mapping(SLAM)owing to their capability of obtaining ranging information actively.Researchers have attempted to use the intensity i...Light Detection and Ranging(LiDAR)sensors are popular in Simultaneous Localization and Mapping(SLAM)owing to their capability of obtaining ranging information actively.Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy.However,before employing LiDAR intensities in SLAM,a calibration operation is usually carried out so that the intensity is independent of the incident angle and range.The range is determined from the laser beam transmitting time.Therefore,the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface.In a complex environment,it is difficult to obtain the incident angle robustly.This procedure also complicates the data processing in SLAM and as a result,further application of the LiDAR intensity in SLAM is hampered.Motivated by this problem,in the present study,we propose a Hyperspectral LiDAR(HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM.HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements.Owing to the design of the laser,the eight-channel range and intensity were collected with the same incident angle and range.According to the laser beam radiation model,the ratio values between two randomly selected channels’intensities at an identical target are independent of the range information and incident angle.To test the proposed method,the HSL was employed to scan a wall with different coloured papers pasted on it(white,red,yellow,pink,and green)at four distinct positions along a corridor(with an interval of 60 cm in between two consecutive positions).Then,a ratio value vector was constructed for each scan.The ratio value vectors between consecutive laser scans were employed to match the point cloud.A classic Iterative Closest Point(ICP)algorithm was employed to estimate the HSL motion using the range information from the matched point clouds.According to the test results,we found that pink and green papers were distinctive at 650,690,and 720 nm.A ratio value vector was constructed using 650-nm spectral information against the reference channel.Furthermore,compared with the classic ICP using range information only,the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation.For the best case in the field test,the proposed method enhanced the heading angle estimation by 72%,and showed an average 25.5%improvement in a featureless spatial testing environment.The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.展开更多
The properties studying results of ceramics based on strontium,calcium and sodium niobates,which vary from the conditions of synthesis,sintering,and mechanical processing,are presented.The evolution of the grain struc...The properties studying results of ceramics based on strontium,calcium and sodium niobates,which vary from the conditions of synthesis,sintering,and mechanical processing,are presented.The evolution of the grain structure of objects is traced depending on their phase filling during concentration changes in the composition and thermodynamic prehistory(preparation conditions).展开更多
基金supported in part by the Strategic Research Council at the Academy of Finland project“Competence Based Growth Through Integrated Disruptive Technologies of 3D Digitalization,Robotics,Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem(293389,314312),Academy of Finland projects“Estimating Forest Resources and Quality-related Attributes Using Automated Methods and Technologies”(334830,334829)”,“Monitoring and understanding forest ecosystem cycles”(334060)。
文摘Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.
基金funded by the Key Program of the National Natural Science Foundation of China(No.41531177)the National Natural Science Foundation of China(No.41901403)+1 种基金the National Science Fund for Distinguished Young Scholars of China(No.41725005)Academy of Finland,Strategic Research Council at the Academy of Finland is gratefully acknowledged through project(314312)as well as Academy of Finland through projects(334830,334829,300066).
文摘Registration of TLS data is an important prerequisite to overcome the limitations of occlusion.Most existing registration methods rely on stems to determine the transformation parameters.However,the complexity of the registration problem increases dramatically as the number of stems grows.It is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two scans.This paper presents an automatic and fast registration of TLS point clouds in forest areas.It reduces stems by selecting from the overlap areas,which are recovered from the mode-based key points that are detected from crowns.The proposed method was tested in a managed forest in Finland,and was compared with the stem-based registration method without reducing stems.The experiments demonstrated that the mean rotation error was 2.09′,and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm,respectively.Compared with the stem-based method,the proposed method improves the registration efficiency significantly(818 s vs 96 s)and achieves similar results in terms of the mean registration errors(1.94′for rotation error,0.83 and 7.38 cm for horizontal and vertical translation error,respectively).
基金Academy of Finland projects“Centre of Excellence in Laser Scanning Research(CoE-LaSR)(307362)”Strategic Research Council project“Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization,Robotics,Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem(314312)+3 种基金Additionally,Chinese Academy of Science(181811KYSB20160113,XDA22030202)Beijing Municipal Science and Technology Commission(Z181100001018036)Shanghai Science and Technology Foundations(18590712600)Jihua lab(X190211TE190)are acknowledged.
文摘Light Detection and Ranging(LiDAR)sensors are popular in Simultaneous Localization and Mapping(SLAM)owing to their capability of obtaining ranging information actively.Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy.However,before employing LiDAR intensities in SLAM,a calibration operation is usually carried out so that the intensity is independent of the incident angle and range.The range is determined from the laser beam transmitting time.Therefore,the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface.In a complex environment,it is difficult to obtain the incident angle robustly.This procedure also complicates the data processing in SLAM and as a result,further application of the LiDAR intensity in SLAM is hampered.Motivated by this problem,in the present study,we propose a Hyperspectral LiDAR(HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM.HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements.Owing to the design of the laser,the eight-channel range and intensity were collected with the same incident angle and range.According to the laser beam radiation model,the ratio values between two randomly selected channels’intensities at an identical target are independent of the range information and incident angle.To test the proposed method,the HSL was employed to scan a wall with different coloured papers pasted on it(white,red,yellow,pink,and green)at four distinct positions along a corridor(with an interval of 60 cm in between two consecutive positions).Then,a ratio value vector was constructed for each scan.The ratio value vectors between consecutive laser scans were employed to match the point cloud.A classic Iterative Closest Point(ICP)algorithm was employed to estimate the HSL motion using the range information from the matched point clouds.According to the test results,we found that pink and green papers were distinctive at 650,690,and 720 nm.A ratio value vector was constructed using 650-nm spectral information against the reference channel.Furthermore,compared with the classic ICP using range information only,the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation.For the best case in the field test,the proposed method enhanced the heading angle estimation by 72%,and showed an average 25.5%improvement in a featureless spatial testing environment.The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.
基金The study was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation(State task in the field of scientific activity,sci-entific project No.0852-2020-0032),(BA30110/20-3-07IF).
文摘The properties studying results of ceramics based on strontium,calcium and sodium niobates,which vary from the conditions of synthesis,sintering,and mechanical processing,are presented.The evolution of the grain structure of objects is traced depending on their phase filling during concentration changes in the composition and thermodynamic prehistory(preparation conditions).
