Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ...Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.展开更多
Aims The latest China Vegetation Classification System(China-VCS)for natural/semi-natural vegetation has eight hierarchical levels:Association<Association-group<Subformation<Formation<Formation-group<Ve...Aims The latest China Vegetation Classification System(China-VCS)for natural/semi-natural vegetation has eight hierarchical levels:Association<Association-group<Subformation<Formation<Formation-group<Vegetation-subtype<Vegetation-type<Vegetation-type-group.The classification is based on dominant species and their growth forms and has been completed at the formation level.The principal challenge today in Chinese vegetation classification is to develop the China-VCS at levels below the formation in a way that is consistent with current international standards.We explored the following question:how can existing vegetation plot data help develop the China-VCS and improve its compatibility with other international classification systems?Methods We compiled 401 plots having plant cover and/or aboveground biomass measurements collected in six Stipa steppe formations and divided them into those with cover data(299 plots)and/or biomass data(283 plots).We applied a combination of hierarchical clustering and ordination to partition the cover and biomass data sets into formations and constituent associations.We then used supervised noise clustering to improve the classification and to identify the core plots representing each association.Diagnostic species were also identified at both association and formation levels.Finally,we compared the classification results based on cover and biomass data sets and combined these results into a comprehensive classification framework for the six formations.Important Findings Our results using cover data were comparable with those using biomass data at both formation and association levels.Three Stipa formations were classified into associations based on cover data,two based on biomass data and one based on both biomass and cover data.Twenty-seven associations were defined and proposed within the six formations,using cover or biomass data as consistent classification sections(CCSs).Both dominant species in the dominant stratum and diagnostic species from multiple strata of the core plots were used to characterize vegetation types at both formation and association levels,improving the compatibility of our classification with the International Vegetation Classification.Temperature and precipitation were found to be important climatic factors determining the distribution pattern and species composition of Stipa-dominated vegetation.We propose a framework for plotbased vegetation classification in the China-VCS,using our work with Stipa-dominated steppe vegetation as an example.We applied the concept of CCS to make optimal use of available data representing both plant cover and biomass.This study offers a model for developing the China-VCS to the association level in a way that is consistent with current international standards.展开更多
基金supported in part by the National Nature Science Foundation of China(62273239,62103283).
文摘Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.
基金The work was supported by‘Strategic Priority Research Program’of the Chinese Academy of Sciences[XDA19050402]National Key Basic Research Programs of China[2015FY210200,2016YFC0502602]+1 种基金National Natural Science Foundation of China[41373081]This work was also supported by the China Scholarship Council[201604910318].
文摘Aims The latest China Vegetation Classification System(China-VCS)for natural/semi-natural vegetation has eight hierarchical levels:Association<Association-group<Subformation<Formation<Formation-group<Vegetation-subtype<Vegetation-type<Vegetation-type-group.The classification is based on dominant species and their growth forms and has been completed at the formation level.The principal challenge today in Chinese vegetation classification is to develop the China-VCS at levels below the formation in a way that is consistent with current international standards.We explored the following question:how can existing vegetation plot data help develop the China-VCS and improve its compatibility with other international classification systems?Methods We compiled 401 plots having plant cover and/or aboveground biomass measurements collected in six Stipa steppe formations and divided them into those with cover data(299 plots)and/or biomass data(283 plots).We applied a combination of hierarchical clustering and ordination to partition the cover and biomass data sets into formations and constituent associations.We then used supervised noise clustering to improve the classification and to identify the core plots representing each association.Diagnostic species were also identified at both association and formation levels.Finally,we compared the classification results based on cover and biomass data sets and combined these results into a comprehensive classification framework for the six formations.Important Findings Our results using cover data were comparable with those using biomass data at both formation and association levels.Three Stipa formations were classified into associations based on cover data,two based on biomass data and one based on both biomass and cover data.Twenty-seven associations were defined and proposed within the six formations,using cover or biomass data as consistent classification sections(CCSs).Both dominant species in the dominant stratum and diagnostic species from multiple strata of the core plots were used to characterize vegetation types at both formation and association levels,improving the compatibility of our classification with the International Vegetation Classification.Temperature and precipitation were found to be important climatic factors determining the distribution pattern and species composition of Stipa-dominated vegetation.We propose a framework for plotbased vegetation classification in the China-VCS,using our work with Stipa-dominated steppe vegetation as an example.We applied the concept of CCS to make optimal use of available data representing both plant cover and biomass.This study offers a model for developing the China-VCS to the association level in a way that is consistent with current international standards.
