High-Accuracy Synchronous Extraction Algorithm of Star and Celestial Body Features for Optical Navigation Sensor

Abstract

This paper provides an optimal high-performance image processing algorithm for a miniaturized independent optical navigation sensor, which combines the functions of a star tracker and a navigation camera. This novel image processing algorithm is capable of extracting two different types of optical navigation measurements from a raw image. The aim is to simultaneously extract stars and target celestial body features with high accuracy and reliability to estimate observer-to-body relative position in subsequent navigation process. This paper presents star and celestial body imaging models and a novel slope edge model. We propose an high-performance algorithm to achieve the synchronous extraction of star and celestial body image features based on the aforementioned models. Double-window variance difference method is proposed to segment and classify stars and edge image regions of a celestial body with strong robustness. The sub-pixel level position of star centroid and celestial body edges are then simultaneously extracted by using the same operator on the basis of the consistency of the derivative distribution of star and celestial body edge profiles. The edge extraction deviation when using the slope edge model is also analyzed and compensated, and the accuracy of the celestial body edge extraction is improved to a higher level. The proposed algorithm has excellent feature extraction performance in terms of qualitative and quantitative measurements. This paper has established a technical foundation for the development of the miniaturized independent optical navigation sensor, which is low cost, light weight and has flexible applicability due to its high accuracy and robustness.