Multi-Floor Indoor Localization Based on Multi-Modal Sensors

Zhou, Guangbing; Xu, Shugong; Zhang, Shunqing; Yu, Wei; Xiang, Chenlu

doi:10.3390/s22114162

Cited by 18 publications

(4 citation statements)

References 36 publications

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“…The deep fuzzy forest can inherit the merits of decision trees and deep neural networks within an end-to-end trainable architecture. Guangbing Zhou et al [ 110 ] combine WiFi, vision, and LIDAR for the indoor localization of mobile robots.The WiFi-based RSSI fingerprinting localization method was used for coarse area estimation.…”

Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

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Section: Overview Of Single Sensor Sensing Technologiesmentioning

confidence: 99%

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Liu,

Wang,

Xie

et al. 2024

Sensors

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“…However, WiFi signals are susceptible to interference and signal strength variability. To address this, methods using sensor fusion, such as cameras, LiDAR, and WiFi footprints, for localization have been conducted [25], [26].…”

Section: Related Workmentioning

confidence: 99%

Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

Kim,

Kang,

Lee

et al. 2024

IEEE Access

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In recent years, the demand for delivery services has increased by applying robot technology in various fields such as food services, logistics, hospitals, and hotel business. However, it is still challenging to perform autonomous delivery in multi-floor buildings. Particularly for wheeled robots, the use of elevators is essential for indoor last-mile delivery service in buildings. To tackle the problem, We have developed an indoor delivery mobile robot and present its architecture designed for multi-floor environments. The architecture consists of five modules: map management for utilizing an integrated navigation map, localization, path planning, perception, and task planning. The integrated navigation map is generated by combining multi-floor point cloud maps and topological maps based on node graphs for effective localization and path planning. Additionally, the proposed 3D route planning allows inter-floor movement. A feasible path for boarding the elevator can be generated through the perception module, and delivery services to multiple destinations can be repeatedly performed through task planning. Our architecture's effectiveness is demonstrated through a month-long field test in an ordinary building during regular business hours. This study's contributions include a novel architecture for autonomous delivery without human intervention, an integrated map for efficient indoor navigation, and the proven robustness of the system in real-world scenarios.

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“…Because of the multipath effect, reflecting, fading, deep shadowing effect, and the degradation of delay caused by pervasive hindrances and interactive interference, the pattern of signals in indoor surroundings is more complicated than in outside situations. Therefore, researchers are becoming more interested in indoor-localization methods, which are based on static/mobile cameras, Wi-Fi, Inertial Measurement Units (IMU), and other sensor components [4]. Vision-based localization is growing as cameras become more inexpensive and integrated with smart devices.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Localization Based on Deep Attention Learning Model

Abozeid¹,

Taloba²,

El-Aziz³

et al. 2023

Computer Systems Science and Engineering

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Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale Pyramid Module (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25°for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03°for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods.

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Multi-Floor Indoor Localization Based on Multi-Modal Sensors

Cited by 18 publications

References 36 publications

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

An Efficient Indoor Localization Based on Deep Attention Learning Model

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