Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Abstract

Accurate acquisition of spacecraft pose information is a key step in tasks such as space debris removal, rendezvous and docking, and on-orbit maintenance. The application of neural network has deepened for aerospace measurement and control. Studying the parameters’ influence and degree of imaging and neural network on the accuracy of spacecraft pose estimation is necessary and urgent, but there is still little research in related fields. Approximate Bayesian inference in the depth Gaussian process is utilized in this paper to explore above problems, and systematic verification experiments are carried based on the general neural network and spacecraft pose estimation data set. The verification results depict that the estimation errors of position and attitude can be both effectively reduced by selecting a camera whose radial distortion error is less than 240%. Selecting a camera with focal ratio error less than 2.0% can effectively reduce the position estimation error. Selecting a camera whose principal point error is less than 1.0% can effectively improve the accuracy of attitude estimation. The suppression of tangential distortion error does not significantly improve the estimation accuracy of both position and attitude. Parameters about weighted attenuation factor, the number of network layers and characteristic channels of neural network are all sensitive to the accuracy of pose estimation of spacecraft within a specific range. An excellent balance can be achieved between the estimation accuracy and computational overhead when the weight attenuation factor is set between 0.01 and 0.1, and the number of network layers and characteristic channels are set separately between 20~40 and 70~90. It can reach the accuracy of the traditional pose estimation algorithm, and has better processing time stability in real ground test system by using the network architecture of ResNet34 with weight attenuation factor of 0.01, characteristic channels of 90.

Dynamic measurement of a long‐distance moving object using multi‐binocular high‐speed videogrammetry with adaptive‐weighting bundle adjustment

“…In addition, the results of multiple binocular cameras need to be stitched together to obtain the dynamic process of the target in an overall time series. However, the commonly used method of segmented observation and separate calculation for the dynamic parameters may result in lower positioning accuracy, particularly when the target is located at a larger distance from the cameras (H. Zhao et al., 2022). Since overlapping regions are observed repeatedly by neighbouring binocular cameras, bundle adjustment (BA) with joint segments is adopted for the multi‐binocular videogrammetric system.…”