Spacecraft pose estimation and uncertainty modeling method based on intrinsic space

By constructing a probability distribution model in the rotation matrix manifold SO(3) and Euclidean space, using a hierarchical Bayesian framework to decouple uncertainties, and using an end-to-end neural network for spacecraft pose estimation, the problem of insufficient accuracy in complex space environments is solved, achieving high-precision and reliability assessment and improving the safety of autonomous decision-making.

CN121980964BActive Publication Date: 2026-06-19SHANGHAI INSTITUTE OF TECHNICAL PHYSICS CHINESE ACADEMY OF SCIENCES
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Patent Information

Application Number
CN202610417516.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-01
Publication Date
2026-06-19
Estimated Expiration
2046-04-01

AI Technical Summary

Technical Problem

Existing spacecraft attitude estimation methods lack accuracy in complex space environments and struggle to effectively distinguish between accidental uncertainties caused by observation noise and cognitive uncertainties caused by insufficient model knowledge, resulting in insufficient safety of autonomous decision-making.

Method used

A probability distribution model is constructed in the rotation matrix manifold SO(3) and Euclidean space. A hierarchical Bayesian framework is used to decouple random uncertainty and cognitive uncertainty. High-precision pose estimation and reliability quantification are achieved through an end-to-end neural network. Feature extraction and multi-task prediction are performed using EfficientNet and bidirectional feature pyramid network.

Benefits of technology

It improves the accuracy and stability of spacecraft attitude estimation, can clearly characterize the sources of uncertainty, provide reliable risk assessment basis, and enhance the safety of autonomous decision-making.

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Abstract

This invention discloses a spacecraft pose estimation and uncertainty modeling method based on intrinsic space, belonging to the field of spacecraft pose estimation technology. The method involves directly constructing a hierarchical Bayesian probability model on the rotating manifold SO(3) and translation space, using Fisher and Gaussian distributions respectively, and introducing conjugate priors to achieve the fundamental decomposition and quantification of accidental and cognitive uncertainties. An end-to-end multi-task neural network is used to jointly learn the pose probability model parameters, key points, and segmentation information, and an iterative optimization module is employed to improve estimation accuracy. During the training phase, marginal negative log-likelihood and evidence regularization are jointly optimized; during the inference phase, the probability distribution of pose prediction is obtained through analytical marginalization, and the two types of uncertainty are distinguished. This invention improves pose estimation accuracy while outputting well-calibrated uncertainties, providing a reliable basis for the autonomous and safe operation of spacecraft in orbit.
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