Sensor Placement for Triangulation-Based Localization

Tekdas, Onur; Isler, Volkan

doi:10.1109/tase.2009.2037135

Cited by 87 publications

(55 citation statements)

References 12 publications

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“…Some papers discuss the issue of beacon placement. Algorithms to find the best place of a minimum number of beacons to meet a given criterion, most often a minimum positioning error, are proposed in [11], [39], [41]. For BeAMS, there is only one additional constraint: anywhere on the moving area, the receiver has to find at least 3 beacons, not aligned with any other beacon.…”

Section: B System Deploymentmentioning

confidence: 99%

BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

Pierlot

Droogenbroeck

2014

IEEE Trans. Robot.

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Abstract-Positioning is a fundamental issue in mobile robot applications, and it can be achieved in multiple ways. Among these methods, triangulation based on angle measurements is widely used, robust, accurate, and flexible. This paper presents BeAMS, a new active beacon-based angle measurement system used for mobile robot positioning.BeAMS introduces several major innovations. One innovation is the use of a unique unsynchronized channel with On-Off Keying modulated infrared signals to measure angles and to identify the beacons. We also introduce a new mechanism to measure angles: our system detects a beacon when it enters and leaves an angular window. We show that the estimator resulting from the center of this angular window provides an unbiased estimate of the beacon angle. A theoretical framework for a thorough performance analysis of BeAMS is provided. We establish the upper bound of the variance and validate this bound through experiments and simulations; the overall error measure of BeAMS is lower than 0.24 deg for an acquisition rate of 10 Hz.In conclusion, BeAMS is a low power, flexible, and robust solution for angle measurement, and a reliable component for robot positioning.

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Section: B System Deploymentmentioning

confidence: 99%

BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

Pierlot

Droogenbroeck

2014

IEEE Trans. Robot.

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“…Recently, the generalized sensor placement problem was proved NP-hard by Tekdas and Isler [5]. The authors also presented an approximation algorithm, to compute the minimum number and placement of sensors so that the localization uncertainty at every point in the workspace is less than a given threshold.…”

Section: Related Workmentioning

confidence: 99%

The Range Beacon Placement Problem for Robot Navigation

Allen

Macmillan

Marinakis

et al. 2014

2014 Canadian Conference on Computer and Robot Vision

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Instrumentation of an environment with sensors can provide an effective and scalable localization solution for robots. Where GPS is not available, beacons that provide position estimates to a robot must be placed effectively in order to maximize a robots navigation accuracy and robustness. Sonar range-based beacons are reasonable candidates for low cost position estimate sensors. In this paper we explore heuristics derived from computational geometry to estimate the effectiveness of sonar beacon deployments given a predefined mobile robot path. Results from numerical simulations and experimentation demonstrate the effectiveness and scalability of our approach.

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“…Geometric Dilution of Precision (GDOP) is one measure relating the uncertainty with distance and relative angles. Tekdas and Isler [12] presented a placement scheme which guarantees that for any target location there are always two sensors whose GDOP is a constant factor of the GDOP achieved by any two sensors from an optimal placement. We do not restrict the estimator to use only two sensors, instead, allow combining all measurements.…”

Section: Related Workmentioning

confidence: 99%

Sensor placement and selection for bearing sensors with bounded uncertainty

Tokekar

Isler

2013

2013 IEEE International Conference on Robotics and Automation

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We study the problem of placing bearing sensors so as to estimate the location of a target in a square environment. We consider sensors with unknown but bounded noise: the true location of the target is guaranteed to be in a 2α-wedge around the measurement, where α is the maximum noise. The quality of the placement is given by the area or diameter of the intersection of measurements from all sensors in the worst-case (i.e. regardless of the target's location). We study the bi-criteria optimization problem of placing a small number of sensors while guaranteeing a worst-case bound on the uncertainty.Our main result is a constant-factor approximation: We show that in general when α ≤ π 4 , at most 9n * sensors placed on a triangular grid has diameter and area uncertainty of at most 5.88U * D and 7.76U * A respectively, where n * , U * D and U * A are the number of sensors, diameter and area uncertainty of an optimal algorithm. In obtaining these results, we present some structural properties which may be of independent interest. We also show that in the triangular grid placement, only a constant number of sensors need to be activated to achieve the desired uncertainty, a property that can be used for designing energy/bandwidth efficient sensor selection schemes.

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Sensor Placement for Triangulation-Based Localization

Cited by 87 publications

References 12 publications

BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

The Range Beacon Placement Problem for Robot Navigation

Sensor placement and selection for bearing sensors with bounded uncertainty

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