Cooperative localization uses odometry and inter-robot measurements to estimate the motion of a robot team. These measurements describe only relative geometry, but the team's estimated position and orientation can drift together over time, i.e., gauge drift. In this work, we study correction of this gauge drift using a radio attenuation map registered in the same world coordinate frame used for robot poses. Specifically, we propose Radio-to-Pose Information (RPI), an interface that converts power residuals and their pose derivatives into a Gaussian information matrix and vector pair. RPI builds on the insight that received power is sensitive to shared displacement in the world map. The pose estimator uses the interface together with odometry and inter-robot measurements to update poses and minimize uncertainty. We characterize the shared motions detectable from the map and observed links. For an uncertain shared map, RPI keeps correlations across robots and time and reproduces joint Gaussian pose estimates and covariances. Across 1000 simulated Gaussian trials, RPI matches the results of joint estimation. Across 32 paired trials with a registered map, RPI reduces common-position RMSE from 0.542m to 0.140m in a 50×50m arena compared with a baseline that uses radio only to weight relative measurements.
Figures & tables
Fig. 1: In the left map, when there is no registered attenuation map, equal-length links have identical distance-only predictions. In the right map, when a registered map is present, equal-length links have different power predictions. Colors indicate attenuation in dB/m.
Fig. 2: This graph represents the robot team at each time step. Solid links represent measurements between robots at the same time, while dashed arrows represent odometry of the same robot across consecutive time steps. Each residual rk(X) compares a recorded measurement with the measurement predicted by the poses.
Fig. 3: RPI converts power measurements and map information into (Λ,η) for pose estimation. Updated poses feed back to the radio module for relinearization. Revised messages replace previous contributions.
Fig. 4: Gaussian shared-map test over 1,000 paired trials. (a) Mean terminal centroid error versus radio-noise standard deviation. (b) Trajectory and terminal-centroid NEES per coordinate, with nominal value one. (c) Covariance difference relative to Joint. Error bars are 95% intervals from trial means.
Fig. 5: Results from 32 paired nonlinear trials with correct and perturbed maps: (a) common-position RMSE, (b) relative-position RMSE, and (c) terminal common uncertainty. RMSE uses the final third of each trajectory. Error bars show 95% intervals computed from trial means.
Fig. 6: Directional map ablation over 32 paired trials. Final-third common x/y RMSE for maps with translation-information ranks zero, one, and two. Error bars show 95% intervals across trials.
The cooperative localization (CL) problem in heterogeneous robotic systems with different measurement capabilities is investigated in this work. In practice, heterogeneous sensors lead to directed and sparse measurement topologies, whereas most existing CL approaches rely on multilateral localization with restrictive multi-neighbor geometric requirements. To overcome this limitation, we enable pairwise relative localization (RL) between neighboring robots using only mutual measurement and odometry information. A unified data-driven adaptive RL estimator is first developed to handle heterogeneous and unidirectional measurements. Based on the convergent RL estimates, a distributed pose-coupling CL strategy is then designed, which guarantees CL under a weakly connected directed measurement topology, representing the least restrictive condition among existing results. The proposed method is independent of specific control tasks and is validated through a formation control application and real-world experiments.
Kunrui Ze, Wei Wang, Guibin Sun +3
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Zhongguancun Laboratory, Beijing 100083, China
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.
Qixin De, Ao Zhuang, Yechen Zhang +2
Shanghai Jiao Tong University, Shanghai 200240, China
Cooperative localization (CL) enables accurate position estimation in multi-robot systems operating in GPS-denied environments. This paper presents a comparative study of five CL approaches: Centralized Cooperative Localization (CCL), Decentralized Cooperative Localization (DCL), Sequential Cooperative Localization (StCL), Covariance Intersection (CI), and Standard Cooperative Localization (Standard-CL). All methods are implemented in ROS and evaluated through Monte Carlo simulations under two conditions: weak data association and robust detection. Our analysis reveals fundamental trade-offs among the methods. StCL and Standard-CL achieve the lowest position errors but exhibit severe filter inconsistency, making them unsuitable for safety-critical applications. DCL demonstrates remarkable stability under challenging conditions due to its measurement stride mechanism, which provides implicit regularization against outliers. CI emerges as the most balanced approach, achieving near-optimal consistency while maintaining competitive accuracy. CCL provides theoretically optimal estimation but shows sensitivity to measurement outliers. These findings offer practical guidance for selecting CL algorithms based on application requirements.
Nivand Khosravi, Rodrigo Ventura, Meysam Basiri
Department of Electrical and Computer Engineering, Institute for Systems and Robotics (ISR), Instituto Superior T´ecnico, Lisbon, Portugal