Identifying and localizing robots in a multi-robot system environment

Kato, Koji; Ishiguro, Hiroshi; Barth, Matthew

doi:10.1109/iros.1999.812805

Cited by 33 publications

(29 citation statements)

References 7 publications

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“…Some sensors are able to estimate accurately the distance between robots, such as the ultrasound wave used in the milibots project [10]. Other sensors estimate the bearing of the observed robot such as the omnidirectional video cameras used in [13,9], or both the distance and relative bearing, with stereo vision and active lighting in [3], and vision and laser range scanners in [2]. Finally, sensors are able to estimate even the orientation of the observed robot, in addition to the distance and relative bearing [15,24].…”

Section: Related Workmentioning

confidence: 99%

Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

Roumeliotis¹,

Rekleitis²

2004

Autonomous Robots

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Abstract. This paper examines the problem of cooperative localization for the case of large groups of mobile robots. A Kalman filter estimator is implemented and tested for this purpose. The focus of this paper is to examine the effect on localization accuracy of the number N of participating robots and the accuracy of the sensors employed. More specifically, we investigate the improvement in localization accuracy per additional robot as the size of the team increases. Furthermore, we provide an analytical expression for the upper bound on the positioning uncertainty increase rate for a team of N robots as a function of N , the odometric and orientation uncertainty for the robots, and the accuracy of a robot tracker measuring relative positions between pairs of robots. The analytical results derived in this paper are validated both in simulation and experimentally for different test cases.

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Section: Related Workmentioning

confidence: 99%

Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

Roumeliotis¹,

Rekleitis²

2004

Autonomous Robots

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“…The two Jacobian H i and H j in the update equations (9) and (10), are obtained from the expression in (11): …”

Section: A Relative Bearingmentioning

confidence: 99%

“…Their approach has been validated through real experiments showing a drastic improvement in localization speed and accuracy when compared to conventional single robot localization. Other approaches take advantage of relative observations for multi-robot localization [8], [9], [10], [13], [14], [15]. In [9] a method based on a combination of maximum likelihood estimation and numerical optimization was introduced.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Localization Using Relative Observations

Martinelli

Pont

Siegwart

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

164

111

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Abstract-In this paper we consider the problem of simultaneously localizing all members of a team of robots. Each robot is equipped with proprioceptive sensors and exteroceptive sensors. The latter provide relative observations between the robots. Proprioceptive and exteroceptive data are fused with an Extended Kalman Filter. We derive the equations for this estimator for the most general relative observation between two robots. Then we consider three special cases of relative observations and we present the structure of the filter for each case. Finally, we study the performance of the approach through many accurate simulations.

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“…A variety of alternative sensors has been considered. For example, [8] use robots equipped with omnidirectional vision cameras in order to identify and localize each other. In contrast, [2] use a pair of robots, one equipped with an active stereo vision and one with active lighting to localize.…”

Section: Previous Workmentioning

confidence: 99%

Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy

Rekleitis

Dudek

Milios

IEEE/RSJ International Conference on Intelligent Robots and System

127

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This paper examines the tradeoffs between different classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots 1 .

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Identifying and localizing robots in a multi-robot system environment

Cited by 33 publications

References 7 publications

Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

Propagation of Uncertainty in Cooperative Multirobot Localization: Analysis and Experimental Results

Multi-Robot Localization Using Relative Observations

Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy

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