Integrated Positioning and Obstacle Avoidance for Multi-Quadrotors in an Indoor Office Environment

Yan, Jiaxin; Yang, Xiaofei; Ye, Hui; Wang, Zhe

doi:10.23919/chicc.2019.8866655

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…The proposed system was applied to a swarm of six micro aerial vehicles (MAVs) performing a light show in an indoor exhibition hall. In Reference [ 46 ], an inertial navigation system (INS) combined with UWB was used as an integrated positioning system for multi-quadrotor positioning and obstacle avoidance. Position estimates were obtained via an unscented Kalman filter (UKF).…”

Section: Indoor Positioning Of Unmanned Air Vehicles (Uavs)mentioning

confidence: 99%

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Khalaf-Allah

2020

Sensors

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In this article, the four-anchor time difference of arrival (TDoA)-based three-dimensional (3D) positioning by particle filtering is addressed. The implemented particle filter uses 1000 particles to represent the probability density function (pdf) of interest, i.e., the posterior pdf of the target node’s state (position). A resampling procedure is used to generate particles in the prediction step, and TDoA measurements are used to determine the importance, i.e., weight, of each particle to enable updating the posterior pdf and estimating the position of the target node. The simulation results show the feasibility of this approach and the possibility to employ it in indoor positioning applications under the assumed working conditions using, e.g., the ultra-wideband (UWB) wireless technology. Therefore, it is possible to enable unmanned air vehicle (UAV) positioning applications, e.g., inventory management in large warehouses, without the need for an excessive number of anchor nodes.

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Section: Indoor Positioning Of Unmanned Air Vehicles (Uavs)mentioning

confidence: 99%

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Khalaf-Allah

2020

Sensors

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“…In recent years, numerous emerging technologies for indoor pedestrian localization [1] have surfaced. Regarding applicability and effectiveness, the most widely employed positioning technologies can be broadly categorized into two types: inertial navigation positioning technology [2] and wireless positioning technology [3] . Inertial navigation positioning technology operates independently of external information, remaining unaffected by external electromagnetic interference [4] ; however, it is susceptible to the issue of error accumulation.…”

Section: Introductionmentioning

confidence: 99%

Indoor pedestrian fusion localization algorithm based on unscented Kalman filter for UWB/PDR

Zhan,

Gao,

Liu

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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In complex indoor environments, achieving satisfactory pedestrian localization using a single positioning technique proves challenging. For instance, Ultra-Wideband (UWB) positioning encounters non-line-of-sight errors in intricate indoor settings, while Pedestrian Dead Reckoning (PDR) technology with inertial sensors is susceptible to cumulative drift errors over time. Consequently, this paper introduces the Unscented Kalman Filter (UKF) algorithm to integrate UWB technology with PDR technology. The improved PDR-derived pedestrian localization information is employed as the state vector for the UKF algorithm, and the positioning information obtained through enhanced UWB technology serves as the observation vector for the UKF algorithm. This combined approach effectively corrects pedestrian position information, ultimately yielding more accurate pedestrian locations. Research results indicate that the proposed algorithm achieves a root mean square error of 3.64 centimeters. In comparison to a standalone UWB algorithm, this method demonstrates superior positioning accuracy in complex environments.

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Integrated Positioning and Obstacle Avoidance for Multi-Quadrotors in an Indoor Office Environment

Cited by 2 publications

References 14 publications

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes

Indoor pedestrian fusion localization algorithm based on unscented Kalman filter for UWB/PDR

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