Decentralized Localization of Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Leung, Kitty; Barfoot, Timothy D.; Liu, H.

doi:10.1109/tro.2009.2035741

Cited by 98 publications

(77 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In cooperative localization, robots use relative measurements between one another to estimate the state of the robot team (Leung et al, 2009(Leung et al, , 2010a. To use a sub-dataset for this problem the users will need to ignore all landmark measurements (by examining the barcode number identified in each measurement).…”

Section: Cooperative Localizationmentioning

confidence: 99%

The UTIAS multi-robot cooperative localization and mapping dataset

Leung

Halpern

Barfoot

et al. 2011

The International Journal of Robotics Research

View full text Add to dashboard Cite

This paper presents a two-dimensional multi-robot cooperative localization and mapping dataset collection for research and educational purposes. The dataset consists of nine sub-datasets, which can be used for studying problems such as robot-only cooperative localization, cooperative localization with a known map, and cooperative simultaneous localization and mapping (SLAM). The data collection process is discussed in detail, including the equipment we used, how measurements were made and logged, and how we obtained groundtruth data for all robots and landmarks. The format of each file in each sub-dataset is also provided. The dataset is available for download at

show abstract

Section: Cooperative Localizationmentioning

confidence: 99%

The UTIAS multi-robot cooperative localization and mapping dataset

Leung

Halpern

Barfoot

et al. 2011

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…Exactly knowing the position, velocity, attitude and other motion information of each mobile robot is one of the basic premises of the MMR cooperative system [3,4]. This means that it requires the ability of self-localization for each mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

View full text Add to dashboard Cite

Abstract:The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

show abstract

“…This problem has been investigated for a long time and several approaches have been introduced in earlier works [5], [8], [17], [26], [35], [36], [37]. Then, a great effort has been devoted to decentralize the computation among the team members and, simultaneously, to minimize the communication among the robots without deteriorating the localization performance [2], [12], [18], [19], [20], [23], [24], [38]. Specific cases of cooperative localization have been considered both in 2D and in 3D.…”

Section: Introductionmentioning

confidence: 99%

Cooperative visual-inertial sensor fusion: Fundamental equations

Martinelli

Renzaglia

2017

2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

View full text Add to dashboard Cite

Abstract-This paper provides a new theoretical and basic result in the framework of cooperative visual-inertial sensor fusion. Specifically, the case of two aerial vehicles is investigated. Each vehicle is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each vehicle can observe the other vehicle. No additional camera observations (e.g., of external point features in the environment) are considered. First, the entire observable state is analytically derived. This state includes the relative position between the two aerial vehicles (which includes the absolute scale), the relative velocity and the three Euler angles that express the rotation between the two vehicle frames. Then, the basic equations that describe this system are analytically obtained. In other words, both the dynamics of the observable state and all the camera observations are expressed only in terms of the components of the observable state and in terms of the inertial measurements. These are the fundamental equations that fully characterize the problem of fusing visual and inertial data in the cooperative case. The last part of the paper describes the use of these equations to achieve the state estimation through an EKF. In particular, a simple manner to limit communication among the vehicles is discussed. Results obtained through simulations show the performance of the proposed solution, and in particular how it is affected by limiting the communication between the two vehicles.

show abstract

Decentralized Localization of Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Cited by 98 publications

References 27 publications

The UTIAS multi-robot cooperative localization and mapping dataset

The UTIAS multi-robot cooperative localization and mapping dataset

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Cooperative visual-inertial sensor fusion: Fundamental equations

Contact Info

Product

Resources

About