Indoor navigation: state of the art and future trends

El‐Sheimy, Naser; Li, You

doi:10.1186/s43020-021-00041-3

Cited by 158 publications

(44 citation statements)

References 81 publications

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“…A summary of some of the essential works is presented in Tables 2 and 3. Based on all this knowledge and also after reviewing a few more works [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], it was found that there has not been more research work that focused on detecting an invalid predicted location based on the speed of the pedestrians in an indoor environment. So, a model that depends on Recurrent Neural Network and uses the speed of pedestrian navigation to adjust the predicted location label is proposed, which is discussed in Sect.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.

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Section: Related Workmentioning

confidence: 99%

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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“…Sementara jika dilihat berdasarkan Citation Bursts terdapat 10 besar berdasarkan kata kunci. Berdasarkan gambar 6, ditinjau siapa penelitinya, Sisi Zlatanova mendominasi dengan 23 penelitian dengan beberapa penelitiannya seperti [10], [11] dan [12] diikuti oleh El-Sheimy dengan 13 penelitian dengan penelitian terbarunya yaitu [13] serta Asakawa dengan 10 Penelitian.…”

Section: Gambar 2 Cluster Pada Penelitian DI Bidang Navigasi Dalam Ruanganunclassified

Review Pemetaan Domain Pengetahuan Tentang Navigasi Dalam Ruangan Melalui Pendekatan Bibliomatrik

Hadiana

Witanti

Komarudin

2021

JICT

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Indoor navigation has been recognized as a technological innovation in the field of navigation. Due to the limitations of GPS in indoor areas, indoor navigation has become a growing area of research in recent years. Due to the rapid development of research in the field of indoor navigation, various stakeholders require an up-to-date review of the research and implementation of indoor navigation. The purpose of this study is to provide an objective and accurate summary of the domain in indoor navigation research using 1618 papers related to indoor navigation taken from the SCOPUS database from 2016 to 2021 using bibliometric analysis. The results are useful for identifying clusters and research topics in this community, such as mobile robots, indoor navigation systems, 3d pedestrian indoor navigation and other clusters. More importantly, these results can help highlight how research related to indoor navigation has evolved, thereby greatly contributing to understanding the underlying structure of the field. This study offers useful and new insights to encapsulate the status quo of Indoor Navigation knowledge and can be used as a dynamic platform to integrate the future development of Indoor Navigation

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“…To resolve these issues, many studies successfully employed other onboard sensors to provide alternative solutions to the positioning problem in GPS-denied environments [5]. The most commonly used onboard sensors include ultrasonic rangers, laser rangers, light detecting and ranging (Lidar) sensors, and visual cameras [6].…”

Section: Introductionmentioning

confidence: 99%

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

Xuan-Mung

Nguyen

et al. 2022

IEEE Access

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Unmanned aerial vehicles, especially quadcopters, play key roles in many real-world applications and the related quadcopter autonomous control algorithms have attracted a great deal of attention. In this paper, we address the vision-based autonomous landing problem of a quadcopter on a ground moving target. Firstly, we propose a disturbance observer-based control algorithm, consisting of a nonlinear disturbance observer and robust altitude and attitude controllers. This algorithm is based on the quadcopter dynamics model, and its stability is strictly proved using Lyapunov's theory. Secondly, we develop an autonomous landing planner which we test for various landing scenarios to deliver improved reliability and accuracy of the landing mission. These theoretical studies are complemented by a numerical feasibility study, before demonstrating the effectiveness of our approach under actual flight conditions with an experimental quadcopter platform.INDEX TERMS autonomous vehicle, quadcopter, unmanned aerial vehicle, precision landing, moving target, disturbance observer, robust control, mission planing.

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Indoor navigation: state of the art and future trends

Cited by 158 publications

References 81 publications

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Review Pemetaan Domain Pengetahuan Tentang Navigasi Dalam Ruangan Melalui Pendekatan Bibliomatrik

Quadcopter Precision Landing on Moving Targets via Disturbance Observer-Based Controller and Autonomous Landing Planner

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