Multi-Sensor Fusion with Extended Kalman Filter for Indoor Localization system of Multirotor UAV

Karaked, Pawarut; Saengphet, Watcharapol; Tantrairatn, Suradet

doi:10.1109/jcsse54890.2022.9836275

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Historically, indoor UAVs have employed simultaneous localization and mapping (SLAM) in conjunction with lidar [ 10 ] or monocular systems [ 11 , 12 ]. Numerous innovative solutions have been proposed, encompassing VLC-based indoor positioning, multisensory fusion leveraging extended Kalman filters, optical flow-centric systems, and the data amalgamation of the ultrawideband (UWB) and IMU [ 13 , 14 , 15 , 16 ]. Notably, deep learning has recently emerged as a favored solution [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning

Hsieh,

Jhan,

Yeh

et al. 2023

Sensors

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Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor positioning methods offer excellent precision, they often demand significant costs and computational resources. Furthermore, such high functionality can be superfluous for these applications. To address this issue, we present a cost-effective, computationally efficient solution for path following and obstacle avoidance. The UAV employs a down-looking camera for path following and a front-looking camera for obstacle avoidance. This paper refines the carrot casing algorithm for line tracking and introduces our novel line-fitting path-following algorithm (LFPF). Both algorithms competently manage indoor path-following tasks within a constrained field of view. However, the LFPF is superior at adapting to light variations and maintaining a consistent flight speed, maintaining its error margin within ±40 cm in real flight scenarios. For obstacle avoidance, we utilize depth images and YOLOv4-tiny to detect obstacles, subsequently implementing suitable avoidance strategies based on the type and proximity of these obstacles. Real-world tests indicated minimal computational demands, enabling the Nvidia Jetson Nano, an entry-level computing platform, to operate at 23 FPS.

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