The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized ...The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.展开更多
The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light vari...The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light variation caused by the rapid spin of SCBs.In this context,we propose a light prior brightness equalization self-calibration method,which can achieve brightness equalization of SCB images under varying illumination conditions while preserving image details,thereby increasing the number of feature-matching points.First,we design a light prior information function based on the illumination variation law of Lambert’s cosine law.Based on the function,the high-light and low-light areas of SCB images are distinguished.Furthermore,we create a brightness equalization mathematical model that maps the illumination components of high-light and low-light areas.Then,based on the brightness equalization mathematical model,we construct a light prior brightness self-calibration network.The proposed network includes 3 main modules:the illumination component estimation module,brightness self-calibration module,and light prior information prediction module;the proposed network utilizes a multistage illumination sharing approach to achieve separation and optimization of illumination components.Finally,the experimental results show that our method can achieve brightness equalization,markedly increasing the number of correct feature matches.展开更多
文摘The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.
基金support from the National Natural Science Foundation of China(Grant No.U2341214)the Shandong Provincial Natural Science Foundation,China(ZR2023MF006 and ZR2023QF176)the Stable Support Program(HTKJ2024KL502028).
文摘The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light variation caused by the rapid spin of SCBs.In this context,we propose a light prior brightness equalization self-calibration method,which can achieve brightness equalization of SCB images under varying illumination conditions while preserving image details,thereby increasing the number of feature-matching points.First,we design a light prior information function based on the illumination variation law of Lambert’s cosine law.Based on the function,the high-light and low-light areas of SCB images are distinguished.Furthermore,we create a brightness equalization mathematical model that maps the illumination components of high-light and low-light areas.Then,based on the brightness equalization mathematical model,we construct a light prior brightness self-calibration network.The proposed network includes 3 main modules:the illumination component estimation module,brightness self-calibration module,and light prior information prediction module;the proposed network utilizes a multistage illumination sharing approach to achieve separation and optimization of illumination components.Finally,the experimental results show that our method can achieve brightness equalization,markedly increasing the number of correct feature matches.
