Cooperative inchworm localization with a low cost team

Nemsick, Brian E.; Buchan, Austin; Nagabandi, Anusha; Fearing, Ronald S.; Zakhor, Avideh

doi:10.1109/icra.2017.7989748

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…We use this ground truth and distort it with measurement models to simulate real sensor data. We obtain satellite pseudoranges using models described in equations (1), (2), and (3), and inter-agent ranging using equation (4). The noise for LOS satellites and inter-agent ranging is modeled as an additive Gaussian with zero mean.…”

Section: Resultsmentioning

confidence: 99%

“…Developments in CL date back to the early 1990s when initial work by Kurazume et al [2] showed lower accumulated positioning error than dead reckoning. Over the years, various CL approaches have been developed and fall in two general categories: centralized [3], [4], [5], [6] and decentralized [7], [8], [9], [10] algorithms. In a centralized approach, one or multiple fusion centers [11] are used to perform the bulk of computation.…”

Section: Collaborative Localization (Cl)mentioning

confidence: 99%

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Decentralized Collaborative Localization in Urban Environments using 3D-Mapping-Aided (3DMA) GNSS and Inter-Agent Ranging

Tanwar

Gao²

2018

Proceedings of the 31st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

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GPS navigation in urban environments is prone to error sources such as multipath and signal blockage. Shadow matching and likelihood-based 3D-mapping-aided (3DMA) ranging methods have shown high potential in 3DMA GNSS navigation for a single agent in urban environments. However, they suffer from errors such as ambiguity which cannot be reliably mitigated without external sensing and integrated systems. Collaborative localization (CL) is a way to aid navigation in a multi-agent system and is capable of providing the external sensing required to reduce error due to ambiguity in 3DMA approaches. CL algorithms face challenges such as scalability, robustness to noisy sensor data and single point of failure, and operability despite limited inter-agent communication. In this paper, we present a decentralized collaborative localization algorithm which is applicable to sparsely communicating networks, and has limited information exchange. Moreover, the proposed algorithm takes advantage of the variable visibility of the sky and variable building geometry for different agents. We propose a methodology for ambiguity mitigation by constraining an agent's probability distribution using its neighbors. The methodology is based on coupling of agents' GPS measurements with range-only sensors and is applicable to multi-agent systems with these modalities. The proposed method is validated on simulated datasets in an urban area of Champaign, Illinois with multiple agents in a variety of scenarios. We demonstrate the improved performance in terms of positioning accuracy and ambiguity mitigation. We also analyze the impact of network connectivity, and size of network on positioning accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Collaborative Localization (Cl)mentioning

confidence: 99%

Decentralized Collaborative Localization in Urban Environments using 3D-Mapping-Aided (3DMA) GNSS and Inter-Agent Ranging

Tanwar

Gao²

2018

Proceedings of the 31st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

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“…The leaders have high-resolution estimators and help to localize the child robots. Such an approach is further extended in [22]. The main limitation of these approaches is that too many constrains(such as the order of movement) are required on the robot motion.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Cooperative Multi-Robot Localization with EKF

Han,

Chen,

et al. 2018

Preprint

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Multi-robot localization has been a critical problem for robots performing complex tasks cooperatively. In this paper, we propose a decentralized approach to localize a group of robots in a large featureless environment. The proposed approach only requires that at least one robot remains stationary as a temporary landmark during a certain period of time. The novelty of our approach is threefold: (1) developing a decentralized scheme that each robot calculates their own state and only stores the latest one to reduce storage and computational cost; (2) developing an efficient localization algorithm through the extended Kalman filter (EKF) that only uses observations of relative pose to estimate the robot positions;(3) developing a scheme has less requirements on landmarks and more robustness against insufficient observations. Various simulations and experiments using five robots equipped with relative pose-measurement sensors are performed to validate the superior performance of our approach.

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“…Relative measurements between robots can be used in distributed and cooperative localization methods for teams of robots [ 19 , 20 , 21 , 22 , 23 ]. These methods determine relative positions and orientations between robots using a variety of methods, such as sonar distance estimation for teams of robots [ 20 ], cooperative inchworm localization with optical pose estimation [ 19 ], and range-only SLAM for multiple micro-air-vehicles [ 22 ]. Relative measurements could also be used between wireless sensor nodes and robots for localization.…”

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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Cooperative inchworm localization with a low cost team

Cited by 8 publications

References 30 publications

Decentralized Collaborative Localization in Urban Environments using 3D-Mapping-Aided (3DMA) GNSS and Inter-Agent Ranging

Decentralized Collaborative Localization in Urban Environments using 3D-Mapping-Aided (3DMA) GNSS and Inter-Agent Ranging

Decentralized Cooperative Multi-Robot Localization with EKF

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

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