Omni-Swarm: A Decentralized Omnidirectional Visual–Inertial–UWB State Estimation System for Aerial Swarms

Xu, H.; Zhang, Yichen; Zhou, Boyu; Wang, Luqi; Yao, Xinjie; Meng, Guotao; Shen, Shaojie

doi:10.1109/tro.2022.3182503

Cited by 51 publications

(20 citation statements)

References 72 publications

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“…We assume that the robots are equipped with a front-looking depth camera with limited sensing range and that they perform decentralized state estimation, using e.g. [23], to localize themselves and their peers in a common reference frame. Given the estimated relative poses, each UAV exchanges map information with nearby team members within the communication range for more informed decision-making.…”

Section: Methodsmentioning

confidence: 99%

Fast Multi-UAV Decentralized Exploration of Forests

Bartolomei

Teixeira

Chli

2023

IEEE Robot. Autom. Lett.

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Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitute an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of longdistance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV fleet. In this spirit, this work proposes an exploration strategy that enables multiple UAVs to quickly explore complex scenes in a decentralized fashion. By providing the decisionmaking capabilities to each UAV to switch between different execution modes, the proposed strategy is shown to strike a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas in multi-UAV setups up to 30% faster than the state of the art.

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Section: Methodsmentioning

confidence: 99%

Fast Multi-UAV Decentralized Exploration of Forests

Bartolomei

Teixeira

Chli

2023

IEEE Robot. Autom. Lett.

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“…Both methods [13], [14] depend on preset anchors which greatly limits their applications for multivehicle cases. Xu et al [15] proposed a decentralized state estimation system, fusing stereo wide-field-of-view cameras and UWB sensors for a multi-vehicle case. Similarly, Nguyen et al [16] proposed a visual-inertial-UWB multi-vehicle localization system that loosely fuses the UWB and visual-inertial odometry data while tightly fusing all onboard sensors.…”

Section: A Related Workmentioning

confidence: 99%

Range-Aided LiDAR-Inertial Multi-Vehicle Mapping in Degenerate Environment

Jin¹,

Jiang²

2023

Preprint

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This paper presents a range-aided LiDAR-inertial multi-vehicle mapping system (RaLI-Multi). Firstly, we design a multi-metric weights LiDAR-inertial odometry by fusing observations from an inertial measurement unit (IMU) and a light detection and ranging sensor (LiDAR). The degenerate level and direction are evaluated by analyzing the distribution of normal vectors of feature point clouds and are used to activate the degeneration correction module in which range measurements correct the pose estimation from the degeneration direction. We then design a multi-vehicle mapping system in which a centralized vehicle receives local maps of each vehicle and range measurements between vehicles to optimize a global pose graph. The global map is broadcast to other vehicles for localization and mapping updates, and the centralized vehicle is dynamically fungible. Finally, we provide three experiments to verify the effectiveness of the proposed RaLI-Multi. The results show its superiority in degeneration environments.

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“…[22] uses optical flow sensors measuring velocity alongside a UWB and an IMU. Compared to underwater robots using Doppler anemometers [20], [21], [23], the performance of positioning with optical flow sensors may deteriorates with varying illumination or insufficient environmental textures [24]. Therefore, an effective positioning approach only using measurements from a UWB and an IMU is still essential to guarantee the robustness of the whole system [6], [15].…”

Section: A Single-range and Inertia Based Odometrymentioning

confidence: 99%

“…[30]- [33]. More comprehensively, an omnidirectional visual-inertial-UWB framework is further proposed for aerial swarm [24]. Compared to these implementations with optical sensors, the SRIO has a minimal hardware configuration and is still worth furhter investigation.…”

Section: B Observability Based Estimationmentioning

confidence: 99%

SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots

Wang¹,

Mei²,

Yuanjiong³

et al. 2022

Preprint

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Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications. To address this issue, this work first investigates the dissipative nature of flying robots considering aerial drag effects and re-formulates the corresponding positioning problem, which guarantees observability almost surely. On this basis, a dimension-reduced wriggling estimator is proposed accordingly. This estimator slides the estimation horizon in a stepping manner, and output matrices can be approximately evaluated based on the historical estimation sequence. The computational complexity is then further reduced via a dimensionreduction approach using polynomial fittings. In this way, the states of robots can be estimated via linear programming in a sufficiently long interval, and the degree of observability is thereby further enhanced because an adequate redundancy of measurements is available for each estimation. Subsequently, the estimator's convergence and numerical stability are proven theoretically. Finally, both indoor and outdoor experiments verify that the proposed estimator can achieve decimeter-level precision at hundreds of hertz per second, and it is resilient to sensors' failures. Hopefully, this study can provide a new practical approach for self-localization as well as relative positioning of cooperative agents with low-cost and lightweight sensors.

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Omni-Swarm: A Decentralized Omnidirectional Visual–Inertial–UWB State Estimation System for Aerial Swarms

Abstract: If it is the author's pre-published version, changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published version.

Cited by 51 publications

References 72 publications

Fast Multi-UAV Decentralized Exploration of Forests

Fast Multi-UAV Decentralized Exploration of Forests

Range-Aided LiDAR-Inertial Multi-Vehicle Mapping in Degenerate Environment

SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots

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