Multirobot Cooperative Localization Algorithm with Explicit Communication and Its Topology Analysis

Chang, Tsang-Kai; Chen, Shengkang; Mehta, Ankur

doi:10.1007/978-3-030-28619-4_46

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…) is controllable, Ψ i,t is bounded by Theorem 1 in [35], which implies that Φ i,t is also bounded. Proposition 1 is given in a different form in our prior work [12], but a clearer treatment with graph theory is provided here. Proposition 1 states that as long as all the information collected by robot i covers the entire robot team, the information is sufficient enough to localize the entire robot team, which leads to bounded Ψ i,t and bounded Φ i,t as well.…”

Section: We Define That Smentioning

confidence: 99%

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Resilient and consistent multirobot cooperative localization with covariance intersection

Chang,

Chen,

Mehta

2021

Preprint

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Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple interrobot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when inter-robot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.

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Section: We Define That Smentioning

confidence: 99%

“…This paper is a revised and substantially extended version of our previous conference publication [12]. The conference paper aims to formalize the algorithm, but the investigation of the resilience is only presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Resilient and consistent multirobot cooperative localization with covariance intersection

Chang,

Chen,

Mehta

2021

Preprint

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“…CoLo has been used in a published paper for algorithm developemnt [CCM17]. We used These results show that both local state block diagonal approximation (LS-BDA) [LSR16] and centralized extended Kalman filter (LS-Cen) [KRM14] have similar performances but LS-Cen performs slightly better when there is a lack of observation data using CoLo Dataset 4.…”

Section: Algorithm Development and Evaluationmentioning

confidence: 99%

CoLo: A Performance Evaluation System for Multi-robot Cooperative Localization Algorithms

Chen

Mehta

2019

2019 International Conference on Robotics and Automation (ICRA)

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This thesis presents CoLo-a performance evaluation system for two-dimensional cooperative localization algorithms. Multi-robot system have been used in a wide range of applications and cooperative localization is one of the fundamental tasks for mobile multirobot systems. However, developing cooperative localization algorithm is complex and timeconsuming. CoLo is created to reduce cooperative localization algorithm development cycle time. The system consists of two main parts: a physical experiment (CoLo-PE) for data collection and a software analysis tool (CoLo-AT) using real-world datasets to evaluate the performances of users' cooperative localization algorithms. CoLo uses an intuitive algorithm framework to allow researchers to conveniently add their cooperative localization algorithms to it. Instead of creating simulations or designing a new robotic testbed from the ground up, researchers only needed to load their algorithms in CoLo-AT and analyze them using data collected from CoLo-PE. Also, CoLo is aimed to create a standard so that effective comparisons can be made across research on localization algorithm. This paper details the design and operation of the physical experiment (CoLo-PE) which provides users guidelines to create their own robotic testbed with a ROS-based, scalable and affordable robotic team. And the paper explains how the software analysis tool (CoLo-AT) tests algorithms by running simulation processes to recreate the experiment trials using compatible real-world datasets of odometry data, measurement data, and the related groundtruth ii data. Researchers can test their algorithms and compare them with other state-of-the-art algorithms in various settings. CoLo-AT provides insightful metrics, and graphs include location error for localization accuracy and trace of state covariance to detect over-confident estimation results. It also has an animated plot to show the estimated trajectory and actual trajectory of each robot, which presents an intuitive visualization of the algorithm. CoLo has been used in the development of a published localization algorithm, where it was used to test the performance of the algorithm and compared to other existing algorithms objectively. CoLo provided the performance results in various setting and saved much time in experimental validation, which enabled a more rapid algorithm design process.

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“…However, the method can only handle the spurious sensor data with small deviation, but not those with large deviation in malicious attack or non-line-of-sight (NLOS) scenarios. Moreover, in Chang, Chen & Mehta (2020) , a cooperative localization method based on covariance intersection (CI) is proposed and verified in experiments, which has the same problem of not being able to process the spurious sensor data with large deviations. Based on the above research, this article tries to study the cooperative localization problem and improves the robustness of the cooperative localization algorithm for large abnormal measurements.…”

Section: Introductionmentioning

confidence: 99%

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems

Wang

Liu

2023

PeerJ Computer Science

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Cooperative localization is an arising research problem for multi-robot system, especially for the scenarios that need to reduce the communication load of base stations. This article proposes a novel cooperative localization algorithm, which can achieve high accuracy localization by using the relative measurements among robots. To address uncertainty in the measuring robots’ positions and avoid linearization errors in the extended Kalman filter during the measurement update phase, a particle-based approximation method is proposed. The covariance intersection method is then employed to fuse preliminary estimations from different robots, guaranteeing a minimum upper bound for the fused covariance. Moreover, in order to avoid the negative effect of abnormal measurements, this article adopts the Kullback–Leibler divergence to calculate the distances between different estimations and rejects to fuse the preliminary estimations far from the estimation obtained in the prediction stage. Two simulations are conducted to validate the proposed algorithm. Compared with the other three algorithms, the proposed algorithm can achieve higher localization accuracy and deal with the abnormal measurement.

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Multirobot Cooperative Localization Algorithm with Explicit Communication and Its Topology Analysis

Cited by 9 publications

References 14 publications

Resilient and consistent multirobot cooperative localization with covariance intersection

Resilient and consistent multirobot cooperative localization with covariance intersection

CoLo: A Performance Evaluation System for Multi-robot Cooperative Localization Algorithms

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems

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