Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation

2023-10-24

arXiv.org

AI Summary

Joint estimation of aerial target motion states under bearing-only measurements in multi-robot visual cooperative tracking remains challenging due to limited observability and distributed coordination constraints. Method: This paper proposes a spatio-temporal triangulation framework that tightly integrates distributed recursive least squares (DRLS) with geometric triangulation constraints—specifically embedding bearing-only measurement geometry into the estimator’s structure. Contribution/Results: We rigorously prove exponential convergence of the proposed estimator. By explicitly leveraging bearing-only geometric constraints, the method achieves high-accuracy, rapid collaborative motion estimation. Compared to state-of-the-art distributed Kalman filtering (DKF), it demonstrates significant improvements in both estimation accuracy and convergence speed. Comprehensive numerical simulations and real-world airspace experiments validate the system’s robustness and fully autonomous pursuit capability. To our knowledge, this work establishes the first distributed aerial target tracking system relying solely on vision-based bearing measurements.

Abstract

Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based on the framework distributed Kalman filtering (DKF). In this paper, we propose a new optimal bearing-only cooperative estimation algorithm, named spatial-temporal triangulation, based on the method of distributed recursive least squares, which provides a more flexible framework for designing distributed estimators than DKF. The design of the algorithm fully incorporates all the available information and the specific triangulation geometric constraint. As a result, the algorithm has superior estimation performance than the state-of-the-art DKF algorithms in terms of both accuracy and convergence speed as verified by numerical simulation. We rigorously prove the exponential convergence of the proposed algorithm. Moreover, to verify the effectiveness of the proposed algorithm under practical challenging conditions, we develop a vision-based cooperative aerial target pursuit system, which is the first of such fully autonomous systems so far to the best of our knowledge.

Problem

• Multi-robot systems
• Visual teaming
• Target tracking

Innovation

• Temporal Spatial Triangulation
• Distributed Recursive Least Squares
• Directional Information Estimation