A Closed-Form 4-DoF Inter-Robot Pose Estimator using Bearing-only Measurements
For multi-robot systems requiring cooperative localization, this work provides a more efficient and reliable pose estimation method that reduces computational cost and improves observability under challenging motion patterns.
This paper proposes a closed-form 4-DoF inter-robot pose estimator using bearing-only measurements, which relaxes nonlinear constraints for rotation estimation and uses error projection for translation estimation. The method achieves higher accuracy with lower computational cost and includes an observability test module that ensures reliable estimation under a broader range of motion patterns.
Bearing-odometry-based cooperative localization has attracted increasing research interest due to its minimal infrastructure requirements, low communication bandwidth and broad applicability in complex environments. However, existing 6-DoF approaches still face challenges in rapidly obtaining accurate and reliable inter-robot pose estimation, as the system is prone to observability degeneracy under specific motion patterns. To address these issues, we first propose a closed-form 4-DoF inter-robot pose estimator, which relaxes nonlinear constraints for rotations estimation and employs error projection for translations estimation. We then conduct a theoretical analysis of the system's observability, identifying degeneracy under two typical motion patterns: collinear and shape-preserving formations. The analysis further shows that the proposed 4-DoF system requires less stringent motion excitation for observability, enabling reliable estimation under a broader range of cooperative maneuvers. Furthermore, an observability test module is introduced to autonomously determine the optimal estimation instant, eliminating reliance on a predefined fixed-length sliding window. Extensive simulations and real-world experiments demonstrate that the proposed algorithm achieves higher estimation accuracy with significantly low computational cost, and the observability test module ensures estimation reliability while minimizing the data collection interval.