Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.

A stellar gyroscope is an attitude sensor that is composed of a micro star sensor and a Micro-Electro- Mechanical System(MEMS) gyroscope. The attitude is output by fusing the information of the two sensors. Due to the processing of the star image from the star sensor, a measurement delay is introduced and can affect the performance of the fusion of the two sensors. In this paper, a factor graph method that is based on a sliding window is investigated for dealing with the measurement delay. Temporary factors are calculated to temporarily occupy the places of the factors that are under processing, and sliding window incremental smoothing is only performed with a set of factors and states after the delayed measurement has arrived. To reduce the frequency of attitude calculation based on a star image, the keyframe strategy is adopted. Only in keyframes is the attitude solved by using the star image and used as the measurement of the factor. In non-keyframes, only the star positions in the star images are determined, with a robust star tracking method that is aided by the gyroscope. Moreover, a highrate low-latency output framework that is by a gyroscope is proposed for accelerating the attitude output rate. According to the results of the experiments on simulated data, the proposed method can overcome the problem of measurement delay and is more effective than the compared methods. According to the results of the experiments on real data, the proposed method can be used in applications.

Triangulation is a fundamental module in vision based navigation system, which reconstructs the observed 3D point using cameras with known intrinsic and poses. In this article, we adopt the minimization of the angular residual as the criterion of triangulation and a novel structure of multiple-view triangulation is proposed. A multiple-view triangulation problem is divided into several two-view triangulation problems and a linear algorithm is derived to find the explicit closed form solutions of these two-view triangulation problems. Then, the Cramer–Rao lower bound (CRLB) is employed to calculate their covariances, according to which these solutions are fused to obtain the final result of the multiple-view triangulation. Our proposed algorithm is proved to be efficient based on the theoretical analysis, as it is linear and non-iterative. The simulations on both synthetic and real data show that the proposed algorithm can achieve competitive accuracy within less processing time, comparing with the most state-of-art algorithms.

Planetary rovers play a critical role in space exploration missions, where one of the most fundamental algorithms is pose determination. Due to environmental and computational constraints, real-time pose determinations of planetary rovers can only use low-cost techniques, such as visual odometry. In this paper, by employing the angle-based criterion, a novel pose determination algorithm is proposed for visual odometry, which is suitable for any type of central camera. First, the problem is formulated using the Huber kernel function with respect to the angular residuals. Then, an intermediate coordinate system is introduced between the initial estimation and final refinement. In order to avoid being trapped in periodic local minimums, a linear method is used to further align the reference points between the intermediate and camera coordinate systems. Finally, one step refinement is implemented to optimize pose determinations. The theoretical analysis, the synthetic simulations, and the real experiments show that our proposed algorithm can achieve the best accuracies within similar processing times, compared with the most state-of-the-art algorithms, thereby approving the effectiveness of the proposed algorithm used in planetary rover onboard visual odometry.