The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor l...The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.展开更多
In order to improve the positioning accuracy of self-propelled farm vehicles and to meet the requirements of precision agriculture on the accuracy of machines,a positioning method was proposed based on BeiDou satellit...In order to improve the positioning accuracy of self-propelled farm vehicles and to meet the requirements of precision agriculture on the accuracy of machines,a positioning method was proposed based on BeiDou satellite navigation system(BDS)and GPS dual systems with four satellites.The time base and reference coordinates system of BDS and GPS,as well as the transformation between them were discussed in this paper.Two kinds of mathematical models were proposed for the dual-system multi-satellite positioning and dual-system four-satellite positioning.The solution strategies of the proposed model were detailed,and an improved position calculation model of the dual system was developed with modified models.Experimental results demonstrated that the integrated system could enhance the number of visible satellites,expand the scale of the satellite constellation,increase the number of available satellites and improve the positioning accuracy.Besides,the positioning reliability and continuity were also greatly improved.展开更多
文摘The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.
基金This research was supported by National Science and Technology Support Program(MW-2013-SJ011)the Education Department of Heilongjiang Province Science Project(12521373)Heilongjiang Bayi Agricultural University Graduate Innovation Science Project(YJSCX2014-Y52).
文摘In order to improve the positioning accuracy of self-propelled farm vehicles and to meet the requirements of precision agriculture on the accuracy of machines,a positioning method was proposed based on BeiDou satellite navigation system(BDS)and GPS dual systems with four satellites.The time base and reference coordinates system of BDS and GPS,as well as the transformation between them were discussed in this paper.Two kinds of mathematical models were proposed for the dual-system multi-satellite positioning and dual-system four-satellite positioning.The solution strategies of the proposed model were detailed,and an improved position calculation model of the dual system was developed with modified models.Experimental results demonstrated that the integrated system could enhance the number of visible satellites,expand the scale of the satellite constellation,increase the number of available satellites and improve the positioning accuracy.Besides,the positioning reliability and continuity were also greatly improved.
