Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916114347327488 |
|---|---|
| author | Shin, Kihoon Sim, Hyunjae Nam, Seungwon Kim, Yonghee Hu, Jae Kim, Kwang-Ki K. |
| author_facet | Shin, Kihoon Sim, Hyunjae Nam, Seungwon Kim, Yonghee Hu, Jae Kim, Kwang-Ki K. |
| contents | In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization Shin, Kihoon Sim, Hyunjae Nam, Seungwon Kim, Yonghee Hu, Jae Kim, Kwang-Ki K. Robotics Computer Vision and Pattern Recognition Systems and Control Optimization and Control 93C85, 93E11, 93E24, 90C26, 93E10, 62M20, In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance. |
| title | Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control Optimization and Control 93C85, 93E11, 93E24, 90C26, 93E10, 62M20, |
| url | https://arxiv.org/abs/2401.15313 |