As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigati...As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigation accuracy during landing phase.This paper proposes an optimal landmark selection method based on the observability matrix for the Mars probe.Firstly,an observability matrix for navigation system is constructed with Fisher information quantity.Secondly,the optimal configuration of the landmark distribution is given by maximizing the scalar function of the observability matrix.Based on the optimal configuration,the greedy algorithm is used to determine the number of the landmarks at each moment adaptively.In addition,considering the fact that the number of the observable landmarks gradually decreases during the landing process,the convergence threshold of the greedy algorithm is set to a dynamic value regarding landing time.Finally,mathematical simulation verification is conducted,and the results show that the proposed optimal landmark selection method has higher navigation accuracy compared with the random landmark selection method.It can effectively suppress the influence of the measurement model errors and achieve a higher landing accuracy.展开更多
Planetary craters are natural navigation landmarks that widely exist and are easily observed.Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing.Due ...Planetary craters are natural navigation landmarks that widely exist and are easily observed.Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing.Due to the increase in observed crater landmarks and the limitation of onboard computation,the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical navigation.This paper designs a fast crater landmark selection method,which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters,which is different from other landmark selection methods.The factor of measurement uncertainty,which is anisotropic,correlated and nonidentically distributed,is quantified and integrated into selection based on crater pairing detection and localization error evaluation.In addition,the concept of the crater matching confidence factor is introduced,which reflects the possibility of 2D projection measurements corresponding to 3D positions.Combined with the configuration observability factor,the crater landmark selection indicator is formed.Finally,the effectiveness of the proposed method is verified by Monte Carlo simulations.展开更多
An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoo...An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.展开更多
In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited ...In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.展开更多
基金supported by the National Natural Science Foundation of China(62203458)the Stabilisation Support Project of the Bureau of Science and Industry(HTKJ2023KL502012)the Youth Autonomous Innovation Science Fund(ZK23-01).
文摘As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigation accuracy during landing phase.This paper proposes an optimal landmark selection method based on the observability matrix for the Mars probe.Firstly,an observability matrix for navigation system is constructed with Fisher information quantity.Secondly,the optimal configuration of the landmark distribution is given by maximizing the scalar function of the observability matrix.Based on the optimal configuration,the greedy algorithm is used to determine the number of the landmarks at each moment adaptively.In addition,considering the fact that the number of the observable landmarks gradually decreases during the landing process,the convergence threshold of the greedy algorithm is set to a dynamic value regarding landing time.Finally,mathematical simulation verification is conducted,and the results show that the proposed optimal landmark selection method has higher navigation accuracy compared with the random landmark selection method.It can effectively suppress the influence of the measurement model errors and achieve a higher landing accuracy.
基金supported by the National Key Research and Development Program of China(No.2019YFA0706500)the National Natural Science Foundation of China(No.61873302,61973032,U20B2055 and U2037602)+1 种基金the Basic Scientific Research Program of China(No.JCKY2018602B002)the Space Debris Program of China(No.KJSP2020020302)。
文摘Planetary craters are natural navigation landmarks that widely exist and are easily observed.Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing.Due to the increase in observed crater landmarks and the limitation of onboard computation,the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical navigation.This paper designs a fast crater landmark selection method,which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters,which is different from other landmark selection methods.The factor of measurement uncertainty,which is anisotropic,correlated and nonidentically distributed,is quantified and integrated into selection based on crater pairing detection and localization error evaluation.In addition,the concept of the crater matching confidence factor is introduced,which reflects the possibility of 2D projection measurements corresponding to 3D positions.Combined with the configuration observability factor,the crater landmark selection indicator is formed.Finally,the effectiveness of the proposed method is verified by Monte Carlo simulations.
文摘An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.
基金supported by the National Natural Science Foundation of China(No.U2037602)。
文摘In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.
