Indoor Positioning and Navigation

Tomažič, Simon

doi:10.3390/s21144793

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Localization and sensor fusion are interconnected areas as correct trajectory data is a useful output from a filtering system. This output dataset can be used to localize the robot or as a comparison to other trajectories to review error [9]. As seen in the literature, the Kalman Filter is used to fuse various kinds of sensor data for navigation.…”

Section: Kalman Filtermentioning

confidence: 99%

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Peiris,

Lang,

El-Gindy

2024

J. Phys.: Conf. Ser.

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Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

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Section: Kalman Filtermentioning

confidence: 99%

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Peiris,

Lang,

El-Gindy

2024

J. Phys.: Conf. Ser.

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“…Localization is a great essence of the future wireless networks [1,2,3]. In today's era, precise positioning can enable various wireless use cases like indoor navigation [4,5], drone tracking [6], emergency service [7,8], vehicular navigation [9,10], inventory tracking [11,12], and plays a pivotal role in offering the location-based services [13,14]. Among all, vehicular navigation is becoming a new challenge where positioning is of the essence.…”

Section: Introductionmentioning

confidence: 99%

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

Sadhukhan,

Mukherjee,

Das

2024

Preprint

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5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.

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“…The interest in localization of autonomous mobile systems is a highly active research topic that has been facilitated by the developments in advanced mobile and communication technologies [12,17]. Computing the position and the orientation of an object is a key technology that can bring us a step closer to autonomous vehicles, enabling them to manage in an autonomous way traffic situations and signifficantly reduce the number of accidents [7,10].…”

Section: Introductionmentioning

confidence: 99%

An Indoor Localization System for Automotive Driving Competitions

Kilyen,

Lemnariu,

Muntean

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Localization, in both indoor and outdoor settings, represents a problem that has received increased attention lately. This paper presents the development, testing, and validation of an indoor localization system for 1/10 scale vehicles based on the Robot Operating System (ROS) and ArUco marker detection. It has a distributed architecture, consisting of tens of Raspberry Pi (RPi) singleboard computers running ROS nodes and fitted with cameras, that monitor a certain area of the 14x14 m plane. The developed system has been successfully used in three editions of Bosch Future Mobility Challenge, an international student competition, where the participants are required to implement autonomous driving functionalities in an environment resembling a real-life city on small-scale automated vehicles.

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Indoor Positioning and Navigation

Abstract: Recently, the social and commercial interest in location-based services (LBS) has been increasing significantly [...]

Cited by 8 publications

References 18 publications

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Kalman filter-based sensor fusion for Ackermann steering mobile robots

5G Positioning: Advantageous Aspects, Taxonomy, State-of-the-art Review and Future Trends

An Indoor Localization System for Automotive Driving Competitions

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