Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements

Cossette, Charles Champagne; Ayman, Shalaby, Mohammed; Saussié, David; Ny, Jérôme Le; Forbes, James Richard

doi:10.1109/iros47612.2022.9981301

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…Fishberg and How [54] developed a 3-DOF relative 2D pose estimation system among robots, achieving improvements in mean position error solely based on UWB measurements. Cossette et al [55] introduced a method for estimating relative orientations using UWB distance measurements, demonstrating that estimation accuracy can be increased by optimizing the formation geometry of robot formation. To reduce the impact of robot formation geometry and UWB measurement errors on pose estimation, Jones et al [56] modified the state vector using a kinematic bicycle model, thereby facilitating the direct estimation of the lead vehicle's longitudinal speed and effective steering angle.…”

Section: Direct Pose Estimationmentioning

confidence: 99%

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Wang,

Chen,

et al. 2024

Drones

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Fast and accurate pose estimation is essential for the local motion control of robots such as drones. At present, camera-based motion capture (Mocap) systems are mostly used by robots. However, this kind of Mocap system is easily affected by light noise and camera occlusion, and the cost of common commercial Mocap systems is high. To address these challenges, we propose Easy Rocap, a low-cost, open-source robot motion capture system, which can quickly and robustly capture the accurate position and orientation of the robot. Firstly, based on training a real-time object detector, an object-filtering algorithm using class and confidence is designed to eliminate false detections. Secondly, multiple-object tracking (MOT) is applied to maintain the continuity of the trajectories, and the epipolar constraint is applied to multi-view correspondences. Finally, the calibrated multi-view cameras are used to calculate the 3D coordinates of the markers and effectively estimate the 3D pose of the target robot. Our system takes in real-time multi-camera data streams, making it easy to integrate into the robot system. In the simulation scenario experiment, the average position estimation error of the method is less than 0.008 m, and the average orientation error is less than 0.65 degrees. In the real scenario experiment, we compared the localization results of our method with the advanced LiDAR-Inertial Simultaneous Localization and Mapping (SLAM) algorithm. According to the experimental results, SLAM generates drifts during turns, while our method can overcome the drifts and accumulated errors of SLAM, making the trajectory more stable and accurate. In addition, the pose estimation speed of our system can reach 30 Hz.

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Section: Direct Pose Estimationmentioning

confidence: 99%

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Wang,

Chen,

et al. 2024

Drones

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show abstract

“…Several papers have demonstrated the use of multiple tags to achieve observable relative positioning, including the use of two tags and a 9-axis IMU (Cossette et al, 2021), four tags on a quadrotor for tracking a person (Hepp et al, 2016), and three tags on a hexacopter to localize a quadrotor (Güler et al, 2019). For multiple UWB tags, the optimal location of every UAV in order to enhance the observability degree has been investigated in (Cossette et al, 2022). Also, the observability conditions have been derived in (Shalaby et al, 2021) with the help of graph theory.…”

Section: Introductionmentioning

confidence: 99%

Decentralized triangular relative navigation of multiple UAVs based on relative range and inertial measurements

Alizad,

Hadi

2023

Preprint

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In this paper, a novel algorithm for cooperative relative navigation of multiple Unmanned Aerial Vehicles (UAVs) is proposed based on relative range and inertial measurements. In this algorithm, a relative motion estimation model is established for each group of three UAVs that can form a triangle in space. Each group member estimates the relative position and heading angle of the other group members and shares the estimation results with the other group members. When members satisfy the triangle law among their estimated relative position vectors, they can estimate more accurately. After analyzing the observability of the presented model, the necessary conditions for observability are also given, which are less restrictive compared to previous methods. Then, the presented method is compared with the distributed extended Kalman method. The results of both methods are analyzed in the presence of noise, disturbances, and under the condition of disconnection between the members.

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Decentralized triangular relative localization of multiple UAVs based on relative range and inertial measurements

Alizad,

Nobahari

2024

ISA Transactions

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Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements

Cited by 10 publications

References 23 publications

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Decentralized triangular relative navigation of multiple UAVs based on relative range and inertial measurements

Decentralized triangular relative localization of multiple UAVs based on relative range and inertial measurements

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