Indoor Positioning Technology Based on the Fusion of UWB and BLE

Xia, Jingbo; Wu, Yaoxiang; Du, Xingqiang

doi:10.1007/978-3-030-68884-4_18

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Commonly adopted techniques utilizing wireless signals to measure the position and orientation of an object in indoor environments include UWB (ultra-wideband) [6], BLE (Bluetooth low energy) [7], and Wi-Fi [8]. Usually, these approaches measure signal-based parameters such as the time of arrival (TOA), time difference of arrival (TDOA), two-way ranging (TWR), or angle of arrival (AOA).…”

Section: Related Work 21 Indoor Localization and Path Planningmentioning

confidence: 99%

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

Lin,

Chang,

Huang

2024

Drones

View full text Add to dashboard Cite

In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.

show abstract

Section: Related Work 21 Indoor Localization and Path Planningmentioning

confidence: 99%

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

Lin,

Chang,

Huang

2024

Drones

View full text Add to dashboard Cite

show abstract

System Development and Integration of Unmanned Aerial Vehicle Navigation Techniques for Indoor Environments

Chang,

Lin

2024

2024 IEEE International Conference on Industrial Technology (ICIT)

View full text Add to dashboard Cite

Indoor Positioning System with UWB Based on a Digital Twin

Lou

Zhao

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

Ultra-wideband (UWB) technology is used for indoor positioning, but its positioning accuracy is usually degenerated by various obstacles in the indoor environment because of non-line-of-sight (NLOS). Facing the complex and changeable indoor environment, an indoor positioning system with UWB based on a digital twin is presented in this paper. The indoor positioning accuracy is improved with a perception–prediction feedback of cyber-physics space in this indoor positioning system. In addition, an anchor layout method with virtuality–reality interaction and an error mitigation method based on neural networks is put forward in this system. Finally, a case study is presented to validate this indoor positioning system with a significant improvement in positioning accuracy.

show abstract

Indoor Positioning Technology Based on the Fusion of UWB and BLE

Cited by 3 publications

References 13 publications

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

System Development and Integration of Unmanned Aerial Vehicle Navigation Techniques for Indoor Environments

Indoor Positioning System with UWB Based on a Digital Twin

Contact Info

Product

Resources

About