Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm

Xu, H.; Wang, Luqi; Zhang, Yichen; Qiu, Kejie; Shen, Shaojie

doi:10.1109/icra40945.2020.9196944

Cited by 103 publications

(73 citation statements)

References 14 publications

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“…However, in these works, all agents perform relative pose estimation to all other agents, allowing these approaches to only handle a limited number of robots. The same holds for the recent framework by Xu et al [5], which is probably the work in the literature closest to our approach. Distance measurements are fused with vision-based tracking, resulting in a decentralized visual-inertial-UWB fusion framework for relative state estimation.…”

Section: Related Worksupporting

confidence: 83%

“…3) Relative Pose Estimation: Relative pose estimation between agents can be approached in different ways. For example, the transformation T t SiSj between the agents i and j could be estimated directly, as proposed in [5]. However, as illustrated in Fig.…”

Section: Range-based Formation Estimationmentioning

confidence: 99%

“…However, for all vision-based rendezvous approaches, with increasing distance between the UAVs, the observed UAV (or marker) in the camera image reduces to only a few pixels, rendering an accurate estimating of the relative pose of this UAV impossible. Beyond visual sensors, portable UWB-based range sensing technology is gaining increasing popularity in UAV research over the last years [5]. In contrast to UWB anchors firmly installed in the environment, each UAV carries a UWB module, allowing to measure absolute distances between pairs of UAVs using a time-of-flight principle.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Formation Estimation Via Pairwise Distance Measurements

Ziegler

Karrer

Schmuck

et al. 2021

IEEE Robot. Autom. Lett.

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Continuously and reliably estimating the relative configuration of robotic swarms in real-time constitutes a core functionality when pursuing the autonomy of such a swarm. Relying on external positioning systems, such as GPS or motion tracking systems, can provide the required information, but significantly limits the generality of an approach. In this letter, we target formation estimation for autonomous flights of swarms of small UAVs, as they pose particularly challenging restrictions on onboard resources, while opening up a large variety of practical scenarios for a multi-robot setup. While state of the art has been addressing efficient formation estimation, scalability remains limited to only very few agents that can be handled in real-time, with the workload of each agent depending on the total number of agents in the swarm. Aiming for scalable multi-robot systems, here we propose a distributed formation estimation approach, where the computational load of each agent is decoupled from the swarm size. This approach is implemented in a setup with minimal communication effort, requiring only ego-motion estimates from each agent and pairwise distance measurements between them, constraining their configuration globally. Evaluations on swarms of up to 49 UAVs demonstrate the power of our approach to handle large swarms, while keeping the computational load bounded for individual agents and requiring only little data exchange between two robots.

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Section: Related Worksupporting

confidence: 83%

Section: Range-based Formation Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Formation Estimation Via Pairwise Distance Measurements

Ziegler

Karrer

Schmuck

et al. 2021

IEEE Robot. Autom. Lett.

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“…UWB technology has attracted substantial attention due to its high ranging accuracy. Beside the centralized usage of anchor-based systems [13], [14], point-to-point ranging started receiving more attention [4], [15]- [17]. These results show the advantage of UWB for multi-agent systems, but due to the small number of UWB nodes in the experiments, network usage has not yet been explored.…”

Section: Related Workmentioning

confidence: 99%

Distributed TDMA for Mobile UWB Network Localization

Cao

Chen

St-Onge

et al. 2021

IEEE Internet Things J.

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Many applications related to the Internet-of-Things, such as tracking people or objects, robotics, and monitoring require localization of large networks of devices in dynamic, GPS-denied environments. Ultra-WideBand (UWB) technology is a common choice because of its precise ranging capability. However, allowing access and effective use of the shared UWB medium with a constantly changing set of devices faces some particular challenges: high frequency of ranging measurements by the devices to improve system accuracy; network topology changes requiring rapid adaptation; and decentralized operation to avoid single points of failure.In this paper, we propose a novel Time Division Multiple Access (TDMA) algorithm that can quickly schedule the use of the UWB medium by a large network of devices without collisions in local network neighborhoods and avoiding conflicts with hidden terminals, all the while maximizing network usage. Using exclusively the UWB radio network, we realize a decentralized system for synchronization, dynamic TDMA scheduling, and precise relative positioning on a multi-hop network. Our system does not have special nodes (all nodes are equal) and it is sufficiently scalable for real-world applications. Our method can be applied to implement device localization services in a large spaces without GPS and complex topologies, like malls, museums, mines, etc. We demonstrate our method in simulation and on real hardware in an underground parking lot, showing the effectiveness of its TDMA schedule for relative localization.

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“…The overall system requires an autonomous initialization maneuver of up to 60 s before the filter converges, which introduces a large setup time. [20] proposed a fully decentralized visual-inertial-UWB fusion framework for relative state estimation and was tested on five drones. This framework makes an inconvenient assumption for filter initialization, which is that the initial difference in yaw between the observed drones is minimal.…”

Section: Introductionmentioning

confidence: 99%