Lighthouse Localization of Wireless Sensor Networks for Latency-Bounded, High-Reliability Industrial Automation Tasks

Campos, Felipe; Schindler, Craig B.; Kilberg, Brian; Pister, Kristofer S. J.

doi:10.1109/wfcs47810.2020.9114443

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Furthermore, during lighthouse occlusion events, this state estimator could still calculate the position of the SCµM chip by integrating IMU measurements, improving occlusion tolerance. In fact, we demonstrated in [15] the ability of an EKF to provide occlusion tolerance in a lighthouse-IMU sensor fusion system. Sensor fusion would also increase the tracking rate of the system to the IMU's update rate, which could alleviate the aforementioned lighthouse update rate issues.…”

Section: Resultsmentioning

confidence: 98%

Accurate 3D Lighthouse Localization of a Low-Power Crystal-Free Single-Chip Mote

Kilberg

Campos

Maksimovic

et al. 2020

J. Microelectromech. Syst.

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We present a system for centimeter-precision 3 dimensional localization of a 2×3×0.3 mm 3 , 5 mg, wireless systemon-chip by utilizing a temporally-structured infrared illumination scheme generated by a set of base stations. This 3D localization system builds on previous work by adding a second lighthouse station to enable 3D localization and using the integrated wireless radio, making the localization system fully wireless. We demonstrate 3D tracking with mean absolute errors of 1.54 cm, 1.50 cm, and 5.1 cm for the X, Y, and Z dimensions. This is the first time such a lighthouse localization system has been able to localize a monolithic single-chip wireless system.

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Section: Resultsmentioning

confidence: 98%

Accurate 3D Lighthouse Localization of a Low-Power Crystal-Free Single-Chip Mote

Kilberg

Campos

Maksimovic

et al. 2020

J. Microelectromech. Syst.

Self Cite

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“…By knowing the angular speed of the laser sweep and the time difference between the pulses at the receiver, it is possible to calculate the angle of the receiver in reference to the lighthouse. For detailed explanation regarding this technique the readers are referred to [31].…”

Section: A Localization Techniquesmentioning

confidence: 99%

“…Campos et al [31] describe the use of the lighthouse localization with EKF in the conveyor belt industrial application. The transmitting node is located on a conveyor belt containing an open-source wireless sensor mote MIMSY [76] and an optical receiver module for receiving IR laser sweep from the lighthouse.…”

Section: A Light Based Localizationmentioning

confidence: 99%

Constrained Localization: A Survey

Savić¹,

Vilajosana

Watteyne³

2022

IEEE Access

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Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

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“…This localization method also scales well with the number of sensor motes in the system. HTC Vive’s 2015 release of their Lighthouse Base Station, used as the basis for their virtual reality system, led to a resurgence in the use of lighthouse localization in recent years [ 13 ]. This HTC Vive lighthouse localization system can provide millimeter-precision 6 degree of freedom pose information to objects with constellations of infrared photodidoes at 60 Hz, while also being low-cost and efficiently scalable for large numbers of nodes [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Quadrotor-Based Lighthouse Localization with Time-Synchronized Wireless Sensor Nodes and Bearing-Only Measurements

Kilberg

Campos

Schindler

et al. 2020

Sensors

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Some robotic localization methods, such as ultra wideband localization and lighthouse localization, require external localization infrastructure in order to operate. However, there are situations where this localization infrastructure does not exist in the field, such as robotic exploration tasks. Deploying low power wireless sensor networks (WSNs) as localization infrastructure can potentially solve this problem. In this work, we demonstrate the use of an OpenWSN network of miniaturized low power sensor nodes as localization infrastructure. We demonstrate a quadrotor performing laser-based relative bearing measurements of stationary wireless sensor nodes with known locations and using these measurements to localize itself. These laser-based measurements require little computation on the WSN nodes, and are compatible with state-of-the-art 2 mm × 3 mm monolithic wireless system-on-chips (SoCs). These capabilities were demonstrated on a Crazyflie quadcopter using an Extended Kalman Filter and a network of motes running the OpenWSN wireless sensor network stack. The RMS error for X positioning was 0.57 m and the error for Y positioning was 0.39 m. This is the first use of an OpenWSN sensor network to support robotic localization. Furthermore, simulations show that these same measurements could be used for localizing sensor motes with unknown locations in the future.

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Lighthouse Localization of Wireless Sensor Networks for Latency-Bounded, High-Reliability Industrial Automation Tasks

Cited by 6 publications

References 21 publications

Accurate 3D Lighthouse Localization of a Low-Power Crystal-Free Single-Chip Mote

Accurate 3D Lighthouse Localization of a Low-Power Crystal-Free Single-Chip Mote

Constrained Localization: A Survey

Quadrotor-Based Lighthouse Localization with Time-Synchronized Wireless Sensor Nodes and Bearing-Only Measurements

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