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
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inve...Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.展开更多
Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition const...Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.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
Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time...Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time-consuming manual assistance and quality control.This study presents a new fully automatic approach to extract single trees from large-area TLS data.This data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest types.Results:We demonstrated the proposed method on two openly available datasets.First,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots.Second,we successfully extracted 270 trees from one hectare temperate forest.Quantitative validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,respectively.Conclusions:Our method allows automated access to individual tree level information from TLS point clouds.The proposed method is free from restricted assumptions of forest types.It is also computationally efficient with an average processing time of several seconds for one million points.It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.展开更多
As with the fast advances in the technologies of big Earth data and information communication,Web-based 3D GIS system has come a long way from a few years ago.These advances reflect in many aspects of 3D GIS such as h...As with the fast advances in the technologies of big Earth data and information communication,Web-based 3D GIS system has come a long way from a few years ago.These advances reflect in many aspects of 3D GIS such as higher real-time performance,enhanced interactivity,more realistic 3D visualization effect and improved user interface.This paper aims to present a comprehensive and upto-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium APl,taking landslide disaster as an example.Building upon recent advances in WebGL technology,we developed a suite of enhanced 3D spatial analysis functions,including interactive route planning,instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications,and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet.Moreover,professional functions such as landslide susceptibility mapping,landslide monitoring,spatial temporal contingency plan management,landslide information management,personnel and equipment management,and communication are all implemented and integrated in the 3D GIS system.Most of the functions of the system are implemented using open-source projects,which is beneficial to the development of the 3D GIS research community.展开更多
As a state-of-the-art mapping technology,mobile laser scanning(MLS)is increasingly applied to fields such as digital presentations of city environments.However,its application has recently met a bottleneck in data pro...As a state-of-the-art mapping technology,mobile laser scanning(MLS)is increasingly applied to fields such as digital presentations of city environments.However,its application has recently met a bottleneck in data processing.It has been found that conventional methods for geometrically modeling 3D scattered points are inadequate when dealing with large volumes of MLS data.In fact,this is a challenge that has already been noted in the MLS-relevant fields,e.g.remote sensing,robot perception,and pattern recognition.A variety of algorithms under the schematic frame of analysis,modeling and synthesis(AMS)have been developed in these fields.The AMS paradigm is to first extract the implicit geometric primitives within each scan profile by geometrically modeling its 2D scattered points(GM2P).The resultant 2D geometric primitives are then integrated to restore the real 3D geometrical models.In this process,GM2P is a kernel procedure whereby a review of the GM2P algorithms is assumed to be of significance for developing new efficient algorithms for geometrically modeling 3D scattered points.This idea is supported by MLS sampling often being executed via parallel scan profiles.Indeed,the results of the literature review indicate an avenue for methodologically improving MLS in data processing.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
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.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404).
文摘Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.
基金financial support from the National Natural Science Foundation of China(Grant Nos.32171789,32211530031)Wuhan University(No.WHUZZJJ202220)Academy of Finland(Nos.334060,334829,331708,344755,337656,334830,293389/314312,334830,319011)。
文摘Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The proposed method achieved a 3D registration accuracy at a 0.50-cm level in all difficulty categories using static terrestrial acquisitions.In the terrestrial-aerial registration,data sets were collected from different sensors and at different points of time with scene changes,and a registration accuracy at the raw data geometric accuracy level was achieved.These results represent the highest automated registration accuracy and the strictest evaluation so far.The proposed method is applicable in multiple scenarios,such as 1)the global positioning of individual under-canopy observations,which is one of the main challenges in applying terrestrial observations lacking a global context,2)the fusion of point clouds acquired from terrestrial and aerial perspectives,which is required in order to achieve a complete forest observation,3)mobile mapping using a new stop-and-go approach,which solves the problems of lacking mobility and slow data collection in static terrestrial measurements as well as the data-quality issue in the continuous mobile approach.Furthermore,this work proposes a new error estimate that units all parameter-level errors into a single quantity and compensates for the downsides of the widely used parameter-and object-level error estimates;it also proposes a new deterministic point sets registration method as an alternative to the popular sampling methods.
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partially funded by the Scientific Research Foundation of Xidian Universitypart of 3DForMod project(ANR-17-EGAS-0002-01)funded in the frame of the JPI FACCE ERA-GAS call funded under European Union’s Horizon 2020 research and innovation program(grant agreement No.696356).
文摘Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical properties.This task currently is undertaken through laborious and time-consuming manual assistance and quality control.This study presents a new fully automatic approach to extract single trees from large-area TLS data.This data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest types.Results:We demonstrated the proposed method on two openly available datasets.First,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots.Second,we successfully extracted 270 trees from one hectare temperate forest.Quantitative validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,respectively.Conclusions:Our method allows automated access to individual tree level information from TLS point clouds.The proposed method is free from restricted assumptions of forest types.It is also computationally efficient with an average processing time of several seconds for one million points.It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.
基金supported by the National Key Research and Development Program of China under[Grant number 2019YFC1511304].
文摘As with the fast advances in the technologies of big Earth data and information communication,Web-based 3D GIS system has come a long way from a few years ago.These advances reflect in many aspects of 3D GIS such as higher real-time performance,enhanced interactivity,more realistic 3D visualization effect and improved user interface.This paper aims to present a comprehensive and upto-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium APl,taking landslide disaster as an example.Building upon recent advances in WebGL technology,we developed a suite of enhanced 3D spatial analysis functions,including interactive route planning,instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications,and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet.Moreover,professional functions such as landslide susceptibility mapping,landslide monitoring,spatial temporal contingency plan management,landslide information management,personnel and equipment management,and communication are all implemented and integrated in the 3D GIS system.Most of the functions of the system are implemented using open-source projects,which is beneficial to the development of the 3D GIS research community.
文摘As a state-of-the-art mapping technology,mobile laser scanning(MLS)is increasingly applied to fields such as digital presentations of city environments.However,its application has recently met a bottleneck in data processing.It has been found that conventional methods for geometrically modeling 3D scattered points are inadequate when dealing with large volumes of MLS data.In fact,this is a challenge that has already been noted in the MLS-relevant fields,e.g.remote sensing,robot perception,and pattern recognition.A variety of algorithms under the schematic frame of analysis,modeling and synthesis(AMS)have been developed in these fields.The AMS paradigm is to first extract the implicit geometric primitives within each scan profile by geometrically modeling its 2D scattered points(GM2P).The resultant 2D geometric primitives are then integrated to restore the real 3D geometrical models.In this process,GM2P is a kernel procedure whereby a review of the GM2P algorithms is assumed to be of significance for developing new efficient algorithms for geometrically modeling 3D scattered points.This idea is supported by MLS sampling often being executed via parallel scan profiles.Indeed,the results of the literature review indicate an avenue for methodologically improving MLS in data processing.
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.
