Indoor micro navigation utilizing local infrastructure-based positioning

Einsiedler, Jens; Sawade, Oliver; Schaufele, Bernd; Witzke, Marcus; Radusch, Ilja

doi:10.1109/ivs.2012.6232262

Cited by 17 publications

(14 citation statements)

References 22 publications

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“…Indoor micro-navigation systems for enclosed parking garages [14] are based on car-to-infrastructure communication providing layout information of the car park and the coordinates of the destination parking lot. It uses unique signal rates.…”

Section: Indoor Garage Navigation Based On Car-to-infrastructure Commmentioning

confidence: 99%

Expanding Navigation Systems by Integrating It with Advanced Technologies

Domb¹

2020

Geographic Information Systems in Geospatial Intelligence

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Navigation systems provide the optimized route from one location to another. It is mainly assisted by external technologies such as Global Positioning System (GPS) and satellite-based radio navigation systems. GPS has many advantages such as high accuracy, available anywhere, reliable, and self-calibrated. However, GPS is limited to outdoor operations. The practice of combining different sources of data to improve the overall outcome is commonly used in various domains. GIS is already integrated with GPS to provide the visualization and realization aspects of a given location. Internet of things (IoT) is a growing domain, where embedded sensors are connected to the Internet and so IoT improves existing navigation systems and expands its capabilities. This chapter proposes a framework based on the integration of GPS, GIS, IoT, and mobile communications to provide a comprehensive and accurate navigation solution. In the next section, we outline the limitations of GPS, and then we describe the integration of GIS, smartphones, and GPS to enable its use in mobile applications. For the rest of this chapter, we introduce various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices.

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Section: Indoor Garage Navigation Based On Car-to-infrastructure Commmentioning

confidence: 99%

Expanding Navigation Systems by Integrating It with Advanced Technologies

Domb¹

2020

Geographic Information Systems in Geospatial Intelligence

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“…These apps can quickly detect objects, determine the face value of banknotes, or translate an image into sound. However, micro-navigation [10], or traversing the last hundred meters to a destination, is a prospective area of research that remains inchoate given that problems exist between the solution model and the sensing hardware [7].…”

Section: Related Workmentioning

confidence: 99%

“Watch Your Step”: Precise Obstacle Detection and Navigation for Mobile Users Through Their Mobile Service

et al. 2019

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Obstacle detection or navigation is useful for mobile users, especially for the visually impaired. In this paper, we propose a method for detecting obstacles precisely from mobile devices by using a depth camera. We demonstrate the system based on Google Project Tango. The system was developed by employing the Java SDK of a Google Tango mobile device. It mainly uses a built-in infrared (IR) sensor to collect data to build a perception of the surrounding environment and then constructs a matrix to record the distance information. Subsequently, the system collects data from the surrounding environment in real time and analyzes the data. If any potential risk of being exposed to injury exists, then the system delivers a warning message and indicates a safe path for the user. An experiment was designed to evaluate our system. On the basis of the experimental results, we discuss the implications for the design of the system.

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“…The PACE application allows drivers to leave their vehicles at the entry of a parking garage and let them drive and park to free spots autonomously using series sensors, as presented in [18]. This is enabled by cooperation between the vehicles and the infrastructure of the parking garage [19]. Cooperative aspects are, for instance, camera-based off-board vehicle localization, parking spot allocation, maneuver planning (e.g., to avoid deadlocks), or sharing sensor information among vehicles to recognize moving objects like pedestrians.…”

Section: Pace: Parking Autonomously Inmentioning

confidence: 99%

“…The deviation between averaged simulatioñand averaged experiment is determined by finding the positions in both graphs for which the travelled distance is minimal and calculating spatial distance between them. The same method is applied to determine the confidence interval using the spatial distances between all experiments and the averaged experiment according to (19)…”

Section: Metricsmentioning

confidence: 99%

A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems

Massow

Radusch

2018

Journal of Advanced Transportation

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Advanced Driver Assistance Systems (ADAS) were strong innovation drivers in recent years, towards the enhancement of traffic safety and efficiency. Today’s ADAS adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. However, to further enhance their benefit, ADAS need to cooperate in the future, using communication technologies. The resulting combination of vehicle automation and cooperation, for instance, enables solving hazardous situations by a coordinated safety intervention on multiple vehicles at the same point in time. Since the complexity of such cooperative ADAS grows with each vehicle involved, very large parameter spaces need to be regarded during their development, which necessitate novel development approaches. In this paper, we present an environment for rapidly prototyping cooperative ADAS based on vehicle simulation. Its underlying approach is either to bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. We reconcile the aspects of automation and cooperation in simulation by a tradeoff between precision and scalability. Reducing precise mapping of vehicle dynamics below the limits of driving dynamics enables simulating multiple vehicles at the same time. In order to validate this precision, we also present a method to validate the vehicle dynamics in simulation against real world vehicles.

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Indoor micro navigation utilizing local infrastructure-based positioning

Cited by 17 publications

References 22 publications

Expanding Navigation Systems by Integrating It with Advanced Technologies

Expanding Navigation Systems by Integrating It with Advanced Technologies

“Watch Your Step”: Precise Obstacle Detection and Navigation for Mobile Users Through Their Mobile Service

A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems

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