As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses.Accurately extract...The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses.Accurately extracting the distributions of different crop species is of critical importance for improving agricultural production efficiency and ensuring food security.Traditional fine-scale vegetation extraction methods often face significant challenges due to the presence of spectrally similar features and the substantial influence of background interference,which limit their applicability across large areas.As a key phenological stage of angiosperms,flowering is characterized by distinctive flowering times,floral morphology,and canopy spectral signatures,so it is an effective pathway for fine-scale vegetation extraction using remote sensing.Using rapeseed as an example,this study developed a spectral index model for precise flowering vegetation extraction(FI-R)based on Landsat OLI imagery.The model integrates a yellowness index(Blue,Green)and a peak index(Red,Nir and SWIR1)while leveraging the NDVI to mitigate background interference from spectrally similar objects.This approach successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions.The proposed method was tested in five rapeseed cultivation regions worldwide with diverse backgrounds.Validation datasets were generated using GF imagery and the U.S.CDL dataset.The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation,and achieved overall accuracies exceeding 94%in all study areas.Furthermore,FI-R is compatible with other multispectral sensors that have similar band configurations,so it is applicable to rapeseed extraction in broader contexts.The method also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation.展开更多
In this paper, we discuss the precise asymptotics of moving-average process Xt =∞∑j=0 ajEt-j under some suitable conditions, where {εt, t∈ Z} is a sequence j=0 of stationary ALNQD random variables with mean zeros...In this paper, we discuss the precise asymptotics of moving-average process Xt =∞∑j=0 ajEt-j under some suitable conditions, where {εt, t∈ Z} is a sequence j=0 of stationary ALNQD random variables with mean zeros and finite variances.展开更多
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金supported by the National Natural Science Foundation of China(42201339)the“Science for a Better Development of Inner Mongolia”Program of the Bureau of Science and Technology of the Inner Mongolia Autonomous Region,China(2022EEDSKJXM003)。
文摘The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses.Accurately extracting the distributions of different crop species is of critical importance for improving agricultural production efficiency and ensuring food security.Traditional fine-scale vegetation extraction methods often face significant challenges due to the presence of spectrally similar features and the substantial influence of background interference,which limit their applicability across large areas.As a key phenological stage of angiosperms,flowering is characterized by distinctive flowering times,floral morphology,and canopy spectral signatures,so it is an effective pathway for fine-scale vegetation extraction using remote sensing.Using rapeseed as an example,this study developed a spectral index model for precise flowering vegetation extraction(FI-R)based on Landsat OLI imagery.The model integrates a yellowness index(Blue,Green)and a peak index(Red,Nir and SWIR1)while leveraging the NDVI to mitigate background interference from spectrally similar objects.This approach successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions.The proposed method was tested in five rapeseed cultivation regions worldwide with diverse backgrounds.Validation datasets were generated using GF imagery and the U.S.CDL dataset.The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation,and achieved overall accuracies exceeding 94%in all study areas.Furthermore,FI-R is compatible with other multispectral sensors that have similar band configurations,so it is applicable to rapeseed extraction in broader contexts.The method also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation.
文摘In this paper, we discuss the precise asymptotics of moving-average process Xt =∞∑j=0 ajEt-j under some suitable conditions, where {εt, t∈ Z} is a sequence j=0 of stationary ALNQD random variables with mean zeros and finite variances.
