The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

Martinez Sonia - One of the best experts on this subject based on the ideXlab platform.

  • Server-Assisted Distributed Cooperative Localization Over Unreliable Communication Links
    eScholarship University of California, 2018
    Co-Authors: Kia, Solmaz S, Hechtbauer Jonathan, Gogokhiya David, Martinez Sonia
    Abstract:

    This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a Global Coordinate Frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments

  • Server assisted distributed cooperative localization over unreliable communication links
    2017
    Co-Authors: Kia, Solmaz S, Hechtbauer Jonathan, Gogokhiya David, Martinez Sonia
    Abstract:

    This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a Global Coordinate Frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments.Comment: The title has changes from "A partially decentralized EKF scheme for cooperative localization over unreliable communication links" to "Server assisted distributed cooperative localization over unreliable communication links". The presentation of the paper is revised. New example is added. Experimental results are adde

Hyosung Ahn - One of the best experts on this subject based on the ideXlab platform.

  • multi agent localization of a common reference Coordinate Frame an extrinsic approach
    arXiv: Optimization and Control, 2019
    Co-Authors: Quoc Van Tran, Hyosung Ahn
    Abstract:

    This paper studies the problem of multi-agent cooperative localization of a common reference Coordinate Frame in $\mathbb{R}^3$. Each agent in a system maintains a body-fixed Coordinate Frame and its actual \textit{Frame transformation} (translation and rotation) from the Global Coordinate system is unknown. The mobile agents aim to determine their \textit{trajectories of rigid-body motions} (or the Frame transformations, i.e., rotations and translations) with respect to the Global Coordinate Frame up to a common Frame transformation by using local measurements and information exchanged with neighbors. We present two Frame localization schemes which compute the rigid-body motions of the agents with asymptotic stability and finite-time stability properties, respectively. Under both localization laws, the estimates of the Frame transformations of the agents converge to the actual Frame transformations almost Globally and up to an unknown constant transformation bias. Finally, simulation results are provided.

  • multi agent localization of a common reference Coordinate Frame an extrinsic approach
    IFAC-PapersOnLine, 2019
    Co-Authors: Quoc Van Tran, Hyosung Ahn
    Abstract:

    Abstract This paper studies the problem of multi-agent cooperative localization of a common reference Coordinate Frame in ℝ3. Each agent in a system maintains a body-fixed Coordinate Frame and its actual Frame transformation (translation and rotation) from the Global Coordinate system is unknown. The mobile agents aim to determine their trajectories of rigid-body motions (or the Frame transformations, i.e., rotations and translations) with respect to the Global Coordinate Frame up to a common Frame transformation by using local measurements and information exchanged with neighbors. We present two Frame localization schemes which compute the rigid-body motions of the agents with asymptotic stability and finite-time stability properties, respectively. Under both localization laws, the estimates of the Frame transformations of the agents converge to the actual Frame transformations almost Globally and up to an unknown constant transformation bias. Finally, simulation results are provided.

Kia, Solmaz S - One of the best experts on this subject based on the ideXlab platform.

  • Server-Assisted Distributed Cooperative Localization Over Unreliable Communication Links
    eScholarship University of California, 2018
    Co-Authors: Kia, Solmaz S, Hechtbauer Jonathan, Gogokhiya David, Martinez Sonia
    Abstract:

    This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a Global Coordinate Frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments

  • Server assisted distributed cooperative localization over unreliable communication links
    2017
    Co-Authors: Kia, Solmaz S, Hechtbauer Jonathan, Gogokhiya David, Martinez Sonia
    Abstract:

    This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a Global Coordinate Frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments.Comment: The title has changes from "A partially decentralized EKF scheme for cooperative localization over unreliable communication links" to "Server assisted distributed cooperative localization over unreliable communication links". The presentation of the paper is revised. New example is added. Experimental results are adde

Yingfei Diao - One of the best experts on this subject based on the ideXlab platform.

  • distributed self localization for relative position sensing networks in 2d space
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Zhiyun Lin, Yingfei Diao
    Abstract:

    This paper studies the 2D localization problem of a sensor network given anchor node positions in a common Global Coordinate Frame and relative position measurements in local Coordinate Frames between node pairs. It is assumed that the local Coordinate Frames of different sensors have different orientations and the orientation difference with respect to the Global Coordinate Frame are not known. In terms of graph connectivity, a necessary and sufficient condition is obtained for self-localizability that leads to a fully distributed localization algorithm. Moreover, a distributed verification algorithm is developed to check the graph connectivity condition, which can terminate successfully when the sensor network is self-localizable. Finally, a fully distributed, linear, and iterative algorithm based on the complex-valued Laplacian associated with the sensor network is proposed, which converges Globally and gives the correct localization result.

Quoc Van Tran - One of the best experts on this subject based on the ideXlab platform.

  • multi agent localization of a common reference Coordinate Frame an extrinsic approach
    arXiv: Optimization and Control, 2019
    Co-Authors: Quoc Van Tran, Hyosung Ahn
    Abstract:

    This paper studies the problem of multi-agent cooperative localization of a common reference Coordinate Frame in $\mathbb{R}^3$. Each agent in a system maintains a body-fixed Coordinate Frame and its actual \textit{Frame transformation} (translation and rotation) from the Global Coordinate system is unknown. The mobile agents aim to determine their \textit{trajectories of rigid-body motions} (or the Frame transformations, i.e., rotations and translations) with respect to the Global Coordinate Frame up to a common Frame transformation by using local measurements and information exchanged with neighbors. We present two Frame localization schemes which compute the rigid-body motions of the agents with asymptotic stability and finite-time stability properties, respectively. Under both localization laws, the estimates of the Frame transformations of the agents converge to the actual Frame transformations almost Globally and up to an unknown constant transformation bias. Finally, simulation results are provided.

  • multi agent localization of a common reference Coordinate Frame an extrinsic approach
    IFAC-PapersOnLine, 2019
    Co-Authors: Quoc Van Tran, Hyosung Ahn
    Abstract:

    Abstract This paper studies the problem of multi-agent cooperative localization of a common reference Coordinate Frame in ℝ3. Each agent in a system maintains a body-fixed Coordinate Frame and its actual Frame transformation (translation and rotation) from the Global Coordinate system is unknown. The mobile agents aim to determine their trajectories of rigid-body motions (or the Frame transformations, i.e., rotations and translations) with respect to the Global Coordinate Frame up to a common Frame transformation by using local measurements and information exchanged with neighbors. We present two Frame localization schemes which compute the rigid-body motions of the agents with asymptotic stability and finite-time stability properties, respectively. Under both localization laws, the estimates of the Frame transformations of the agents converge to the actual Frame transformations almost Globally and up to an unknown constant transformation bias. Finally, simulation results are provided.