Decentralized multi-robot cooperative localization using covariance intersection

Carrillo-Arce, Luis C.; Nerurkar, Esha D.; Gordillo, José Luís; Roumeliotis, Stergios I.

doi:10.1109/iros.2013.6696534

Cited by 137 publications

(95 citation statements)

References 17 publications

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“…given by equations (2)(3)(4)(5). Additionally, we need to add the two observation functions, which express the constraint that the two quaternions, q 1 and q 2 , are unit quaternions.…”

Section: B Observability Analysismentioning

confidence: 99%

“…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%

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Cooperative visual-inertial sensor fusion: Fundamental equations

Martinelli

Renzaglia

2017

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

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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.

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“…given by equations (2)(3)(4)(5). Additionally, we need to add the two observation functions, which express the constraint that the two quaternions, q 1 and q 2 , are unit quaternions.…”

Section: B Observability Analysismentioning

confidence: 99%

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)

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“…have been used in the distributed estimation architecture. The work for distributed cooperative localization using ground based robots seems to be very extensive (Shi et al 2010, Carrillo-Arce et al 2013, Wanasinghe et al 2014, Savic and Zazo 2013, Nerukar et al 2009, Madhavan et al 2002, Pillonetto and Carpin 2007,Bailey et al 2011) but the work for airborne platforms seems to be somewhat limited (Indelman et al 2012, Qu et al 2010, Qu and Zhang 2011, Melnyk et al 2012, Chen et al 2013, Wan et al 2015. Some of the researchers (Indelman et al 2012, Melnyk et al 2012) have attempted to use vision based sensors for cooperative localization.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Uas Localization Using Lowcost Sensors

Goel

Kealy

Lohani

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.

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“…This strategy can also be tracked in refs. [6,12,23]. 1 In the EKF CL, the estimated states of all agents are interdependent and the correlation is quantified by covariance.…”

Section: Introductionmentioning

confidence: 99%

A Small-Sized Locating Message for Cooperative Localization under Communication Constraint

Wang

Zhang

Liang

2016

International Journal of Advanced Robotic Systems

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When one agent simultaneously measures multiple teammates in cooperative localization, how to share these relative measurements with low-cost communication is interesting. In this chapter, different communication requirements for sharing absolute measurement and relative measurement are analyzed under extended Kalman filtering, whereby we develop an algorithm to convert multiple relative measurements into one virtual absolute measurement. By this, less number of elements are required to be transmitted across the agents. The utilization of multi-relative measurements is changed to the mode of one round communication from conventional mode where the measurements are processed and broadcast one by one. Simulation results demonstrate that, using the virtual absolute measurement, the almost equal localization performance with real relative measurements is obtained.

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Decentralized multi-robot cooperative localization using covariance intersection

Cited by 137 publications

References 17 publications

Cooperative visual-inertial sensor fusion: Fundamental equations

Cooperative visual-inertial sensor fusion: Fundamental equations

Cooperative Uas Localization Using Lowcost Sensors

A Small-Sized Locating Message for Cooperative Localization under Communication Constraint

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