ABSTRACT:This paper presents a method of constructing a measurable virtual reality environment based on ground (lander/rover) panoramic images and orbital imagery. Ground panoramic images acquired by a lander/rover at different azimuth and elevation angles are automatically registered, seamlessly mosaicked and projected onto a cylindrical surface. A specific function is developed for inverse calculation from the panorama back to the original images so that the 3D information associated with the original stereo images can be retrieved or computed. The three-dimensional measurable panorama is integrated into a globe viewer based on NASA World Wind. The techniques developed in this research can be used in visualization of and measuring the orbital and ground images for planetary exploration missions, especially rover missions.

ABSTRACT:The Ultraviolet Forward Spectrometer is a new type of spectrometer for monitoring the vertical distribution of atmospheric trace gases in the global middle atmosphere. It is on the TianGong-2 space laboratory, which was launched on 15 September 2016. The spectrometer uses a solar calibration mode to modify its irradiance. Accurately calculating the solar orientation is a prerequisite of spectral calibration for the Ultraviolet Forward Spectrometer. In this paper, a method of calculating the solar orientation is proposed according to the imaging geometric characteristics of the spectrometer. Firstly, the solar orientation in the horizontal rectangular coordinate system is calculated based on the solar declination angle algorithm proposed by Bourges and the solar hour angle algorithm proposed by Lamm. Then, the solar orientation in the sensor coordinate system is achieved through several coordinate system transforms. Finally, we calculate the solar orientation in the sensor coordinate system and evaluate its calculation accuracy using actual orbital data of TianGong-2. The results show that the accuracy is close to the simulation method with STK (Satellite Tool Kit), and the error is not more than 2%. The algorithm we present does not need a lot of astronomical knowledge, but only needs some observation parameters provided by TianGong-2.