Image-Based Camera Localization Algorithm for Smartphone Cameras Based on Reference Objects

Jabborov, Fazliddin

doi:10.1007/s11277-020-07487-9

Cited by 13 publications

(8 citation statements)

References 30 publications

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“…Its central estimator enhances the accuracy of beacon angle calculations, thus contributing to the system's reliability. However, the automatic gain control mechanism introduces errors, and localization accuracy is susceptible to varying environmental conditions [88]. Mesa et al [89] developed a distance estimation approach that harnesses the power of MultiLayer Perceptrons (MLPs) combined with a trio of reflective optical distance sensors-visible light, ultraviolet, and near infrared.…”

Section: Infrared Sensorsmentioning

confidence: 99%

Robotics Perception and Control: Key Technologies and Applications

Luo,

Zhou,

Zeng

et al. 2024

Micromachines

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The integration of advanced sensor technologies has significantly propelled the dynamic development of robotics, thus inaugurating a new era in automation and artificial intelligence. Given the rapid advancements in robotics technology, its core area—robot control technology—has attracted increasing attention. Notably, sensors and sensor fusion technologies, which are considered essential for enhancing robot control technologies, have been widely and successfully applied in the field of robotics. Therefore, the integration of sensors and sensor fusion techniques with robot control technologies, which enables adaptation to various tasks in new situations, is emerging as a promising approach. This review seeks to delineate how sensors and sensor fusion technologies are combined with robot control technologies. It presents nine types of sensors used in robot control, discusses representative control methods, and summarizes their applications across various domains. Finally, this survey discusses existing challenges and potential future directions.

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Section: Infrared Sensorsmentioning

confidence: 99%

Robotics Perception and Control: Key Technologies and Applications

Luo,

Zhou,

Zeng

et al. 2024

Micromachines

View full text Add to dashboard Cite

show abstract

“…These solutions traditionally utilize data from available enabling technologies to estimate the position of mobile devices or users in the environment. These enabling technologies can be represented by ultrasound [ 2 ], cameras [ 3 ], light sensors [ 4 ], magnetometers [ 5 ], MEMS (Micro-Electro-Mechanical Systems) or IMUs (Inertial Measurement Units) [ 6 , 7 , 8 ] as well as radio receivers [ 9 ]. Each of these technologies has its pros and cons.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these technologies has its pros and cons. For example, positioning using cameras can provide high accuracy; however, it can have relatively high computation complexity compared to systems based on radio signals [ 3 ]. On the other hand, localization based on measurements of magnetic fields can provide high accuracy for moving users with small complexity; however, it is almost useless for static positioning, as position estimates are based mainly on the classification of changes of the magnetic field when a user moves around the area [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Brída

Machaj

Racko

et al. 2021

Sensors

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While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

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“…Various technologies have attracted attention in the area of position estimation, including camera-based systems with image processing [7], measurements of magnetic field fluctuations [8], the use of inertial measurements units implemented in devices [9] and the use of radio signals [10][11][12]. The advantage of using radio signals for positioning is…”

Section: Introductionmentioning

confidence: 99%

Proposal for a Localization System for an IoT Ecosystem

2021

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In the last decade, positioning using wireless signals has gained a lot of attention since it could open new opportunities for service providers. Localization is important, especially in indoor environments, where the widely used global navigation satellite systems (GNSS) signals suffer from high signal attenuation and multipath propagation, resulting in poor accuracy or a loss of positioning service. Moreover, in an Internet of things (IoT) environment, the implementation of GNSS receivers into devices may result in higher demands on battery capacity, as well as increased cost of the hardware itself. Therefore, alternative localization systems that are based on wireless signals for the communication of IoT devices are gaining a lot of attention. In this paper, we provide a design of an IoT localization system, which consists of multiple localization modules that can be utilized for the positioning of IoT devices that are connected thru various wireless technologies. The proposed system can currently perform localization based on received signals from LoRaWAN, ZigBee, Wi-Fi, UWB and cellular technologies. The implemented pedestrian dead reckoning algorithm can process the data measured by a mobile device that is equipped with inertial sensors to construct a radio map and thus help with the deployment of the positioning services based on a fingerprinting approach.

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Image-Based Camera Localization Algorithm for Smartphone Cameras Based on Reference Objects

Cited by 13 publications

References 30 publications

Robotics Perception and Control: Key Technologies and Applications

Robotics Perception and Control: Key Technologies and Applications

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Proposal for a Localization System for an IoT Ecosystem

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