The UTIAS multi-robot cooperative localization and mapping dataset

Leung, Kitty; Halpern, Yoni; Barfoot, Timothy D.; Liu, Hugh Ht

doi:10.1177/0278364911398404

Cited by 89 publications

(47 citation statements)

References 4 publications

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“…The prerecorded dataset, captured by the University of Toronto, the Institute of Aerospace Studies (UTIAS) [30], used a fleet of five identically iRobot Creates. Shown in Figure 1, each mobile robot was equipped with a monocular camera and an encoder.…”

Section: Experiments Setupmentioning

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

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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.

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Section: Experiments Setupmentioning

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

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“…Communication between i and j is allowed at the time of the relative measurement and they share the measurement r The only terms we have not taken care of yet are the correlations between participating and non-participating robots (15). In contrast to [22], we do not rely on communication with other teammates.…”

Section: Relative Measurementsmentioning

confidence: 99%

“…This is because we do not rely on communication with robot k, that carries the terms σ t ki and σ t kj . To arrive at the general form of approximations that maintain the decentralized structure, we rewrite the SchmidtKalman filter update step (13)- (15). For better readability and w.l.o.g.…”

Section: Relative Measurementsmentioning

confidence: 99%

“…We test our approach on the publicly available UTIAS Multi-Robot Cooperative Localization and Mapping Dataset [15]. A fleet of five (two-wheel differential drive) robots get range and bearing measurements with known correspondences to each other and up to 15 distinguishable landmarks.…”

Section: Experiments With Real World Datamentioning

confidence: 99%

“…1. This picture shows a five robot localization problem in an indoor environment according to the publicly available data set recorded by Leung et al [15]. The three robots on the left observe each other, while the rightmost robot observes two landmarks.…”

Section: Introductionmentioning

confidence: 99%

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Recursive Decentralized Collaborative Localization for Sparsely Communicating Robots

Lukas

Schubert

Roumeliotis³

et al.

Robotics: Science and Systems XII

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Abstract-This paper provides a new fully-decentralized algorithm for Collaborative Localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies -which is particularly difficult in situations where sustained synchronous communication between robots cannot be guaranteed. Current approaches suffer from the need for particular communication schemes, extensive bookkeeping of measurements, overlyconservative assumptions, or the restriction to specific measurement models. To the best of our knowledge, the algorithm we propose in this paper is the first one that tracks interrobot correlations while fulfilling all of the following relevant conditions: communication is limited to two robots that obtain a relative measurement, the algorithm is recursive in the sense that it does not require storage of measurements and each robot maintains only the latest estimate of its own pose, and it supports generic measurement models. These particularly hard conditions make the approach applicable to a wide range of multi-robot applications. Extensive experiments carried out using real world datasets demonstrate the improved performance of our method compared to several existing approaches.

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Multiple-Robot Simultaneous Localization and Mapping: A Review

et al. 2015

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Simultaneous localization and mapping (SLAM) in unknown GPS‐denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single‐robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state‐of‐the‐art multiple‐robot systems, with a major focus on multiple‐robot SLAM. Various issues and problems in multiple‐robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

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The UTIAS multi-robot cooperative localization and mapping dataset

Cited by 89 publications

References 4 publications

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

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

Recursive Decentralized Collaborative Localization for Sparsely Communicating Robots

Multiple-Robot Simultaneous Localization and Mapping: A Review

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