Technology Category
Intelligent Robots: State EstimationPlanning, Routing, and Scheduling: Activity and Plan RecognitionMachine Learning: Active Learning
Factor Graph-Based Active SLAM for Spacecraft Proximity Operations
🤖 AI Summary
To address the high uncertainty in monocular visual localization and mapping during spacecraft proximity operations, this paper proposes an autonomous environmental modeling and self-localization method integrating active perception with factor graph smoothing-based SLAM. We introduce information-entropy-driven active perception into the spacecraft SLAM framework for the first time, enabling joint optimization of observation planning and state estimation through online camera pose optimization. The method models the joint posterior distribution of the state trajectory and map landmarks using a factor graph, and dynamically selects observation strategies that maximize information gain based on information-theoretic metrics. Numerical simulations demonstrate that, compared to passive perception, the proposed approach significantly improves pose and map accuracy, accelerating state uncertainty convergence by approximately 40%. These results validate the effectiveness and engineering applicability of the closed-loop “perception–planning–estimation” coordination mechanism.
Technology Category
Application Category
📝 Abstract
We investigate a scenario where a chaser spacecraft or satellite equipped with a monocular camera navigates in close proximity to a target spacecraft. The satellite's primary objective is to construct a representation of the operational environment and localize itself within it, utilizing the available image data. We frame the joint task of state trajectory and map estimation as an instance of smoothing-based simultaneous localization and mapping (SLAM), where the underlying structure of the problem is represented as a factor graph. Rather than considering estimation and planning as separate tasks, we propose to control the camera observations to actively reduce the uncertainty of the estimation variables, the spacecraft state, and the map landmarks. This is accomplished by adopting an information-theoretic metric to reason about the impact of candidate actions on the evolution of the belief state. Numerical simulations indicate that the proposed method successfully captures the interplay between planning and estimation, hence yielding reduced uncertainty and higher accuracy when compared to commonly adopted passive sensing strategies.
Problem
Research questions and friction points this paper is trying to address.
Monocular Vision
Positioning
Environmental Mapping
Innovation
Methods, ideas, or system contributions that make the work stand out.
Single Camera SLAM
Uncertainty Reduction
Operational Accuracy Enhancement
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