The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative po...The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.展开更多
Because it is hard to search similar structure for low similarity unknown structure proteins directly from the Protein Data Bank （PDB） database, 3D-structure is modeled in this paper for secondary structure regular ...Because it is hard to search similar structure for low similarity unknown structure proteins directly from the Protein Data Bank （PDB） database, 3D-structure is modeled in this paper for secondary structure regular fragments （α-Helices, β-Strands） of such proteins by the protein secondary structure prediction software, the Basic Local Alignment Search Tool （BLAST） and the side chain construction software SCWRL3. First, the protein secondary structure prediction software is adopted to extract secondary structure fragments from the unknown structure proteins. Then, regular fragments are regulated by BLAST based on comparative modeling, providing main chain configurations. Finally, SCWRL3 is applied to assemble side chains for regular fragments, so that 3D-structure of regular fragments of low similarity unknown structure protein is obtained. Regular fragments of several neurotoxins are used for test. Simulation results show that the prediction errors are less than 0.06nm for regular fragments less than 10 amino acids, implying the simpleness and effectiveness of the proposed method.展开更多
基金co-supported by the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3023)the National Natural Science Foundation of China(No.12272404)。
文摘The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.
基金Sponsored by the National Natural Science Foundation of China （60374069） and the Excellent Young Scholars Research Fund of Beijing Institute of Technology （000Y01-3）.
文摘Because it is hard to search similar structure for low similarity unknown structure proteins directly from the Protein Data Bank （PDB） database, 3D-structure is modeled in this paper for secondary structure regular fragments （α-Helices, β-Strands） of such proteins by the protein secondary structure prediction software, the Basic Local Alignment Search Tool （BLAST） and the side chain construction software SCWRL3. First, the protein secondary structure prediction software is adopted to extract secondary structure fragments from the unknown structure proteins. Then, regular fragments are regulated by BLAST based on comparative modeling, providing main chain configurations. Finally, SCWRL3 is applied to assemble side chains for regular fragments, so that 3D-structure of regular fragments of low similarity unknown structure protein is obtained. Regular fragments of several neurotoxins are used for test. Simulation results show that the prediction errors are less than 0.06nm for regular fragments less than 10 amino acids, implying the simpleness and effectiveness of the proposed method.
