Vehicle indoor positioning: A survey

Einsiedler, Jens; Radusch, Ilja; Wolter, Katinka

doi:10.1109/wpnc.2017.8250068

Cited by 25 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Localization allows the UAV to know how relatively close the obstacles are to the UAV and the relative location of the detected defects. Simultaneous Localization and Mapping (SLAM) is a popular solution for obstacle avoidance and localization in autonomous UAVs [114][115][116]. SLAM generates a map of the surrounding area and estimates the distance to each obstacle based on sensor data.…”

Section: Obstacle Avoidance and Localizationmentioning

confidence: 99%

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Kulsinskas,

Durdevic,

Ortiz-Arroyo

2021

Energies

View full text Add to dashboard Cite

Interior and exterior wind turbine blade inspections are necessary to extend the lifetime of wind turbine generators. The use of unmanned vehicles is an alternative to exterior wind turbine blade inspections performed by technicians that require the use of cranes and ropes. Interior wind turbine blade inspections are even more challenging due to the confined spaces, lack of illumination, and the presence of potentially harmful internal structural components. Additionally, the cost of manned interior wind turbine blade inspections is a major limiting factor. This paper analyses all aspects of the viability of using manually controlled or autonomous aerial vehicles for interior wind turbine blade inspections. We discuss why the size, weight, and flight time of a vehicle, in addition to the structure of the wind turbine blade, are the main limiting factors in performing internal blade inspections. We also describe the design issues that must be considered to provide autonomy to unmanned vehicles and the control system, the sensors that can be used, and introduce some of the algorithms for localization, obstacle avoidance and path planning that are best suited for the task. Lastly, we briefly describe which non-destructive test instrumentation can be used for the purpose.

show abstract

Section: Obstacle Avoidance and Localizationmentioning

confidence: 99%

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Kulsinskas,

Durdevic,

Ortiz-Arroyo

2021

Energies

View full text Add to dashboard Cite

show abstract

“…distances ← d ABOY m (RSS C , RefPoints, N) 1 : distances ← ∅ 2 : foreach RSSS in RefPoints do 3 : dist A ← 0 4 : for i = 1 : N do 5 : : while (j < coords.size()) do 10 : if ( coords[j] Y == NULL) 11 : j ← j + 2 · i 12 : continue 13 : else if ( coords[j + i] Y == NULL) 14 : j ← j + 2 · i 15 : continue 16 : else 17 :…”

Section: A Algorithmsunclassified

“…Global Navigation Satellite System (GNSS) such as GPS or Galileo is the primary technology for localization in many LBSs [10]. Here, privacy of localization is not an issue because it is performed in a stand-alone GNSS receiver (e.g., incar navigator) which calculates the location locally without any communication with other parties [11,12].…”

Section: Introductionmentioning

confidence: 99%

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Järvinen

Leppäkoski

Lohan

et al. 2019

2019 IEEE European Symposium on Security and Privacy (EuroS&P)

View full text Add to dashboard Cite

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS.In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500x faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day. 448 2019 IEEE European Symposium on Security and Privacy (EuroS&P)

show abstract

“…On the indoor positioning field, comprehensive surveys on related research can be found in [12] and [13]. The methods seen in these publications rely on the combination of diverse sensors and technologies, such as sonar, radar or WiFi; proprietary technologies like iBeacon [14], and so on.…”

Section: Indoor Positioningmentioning

confidence: 99%

High Precision Indoor Navigation for Autonomous Vehicles

Morales

Botsch

Huber³

et al. 2019

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

Autonomous driving is an important trend of the automotive industry. The continuous research towards this goal requires a precise reference vehicle state estimation under all circumstances in order to develop and test autonomous vehicle functions. However, even when lane-accurate positioning is expected from oncoming technologies, like the L5 GPS band, the question of accurate positioning in roofed areas, e. g., tunnels or park houses, still has to be addressed.In this paper, a novel procedure for a reference vehicle state estimation is presented. The procedure includes three main components. First, a robust standstill detection based purely on signals from an Inertial Measurement Unit. Second, a vehicle state estimation by means of statistical filtering. Third, a high accuracy LiDAR-based positioning method that delivers velocity, position and orientation correction data with a mean error of 0.1 m/s, 4.7 cm and 1 • respectively. Runtime tests on a CPU indicates the possibility of real-time implementation.

show abstract

Vehicle indoor positioning: A survey

Cited by 25 publications

References 24 publications

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

High Precision Indoor Navigation for Autonomous Vehicles

Contact Info

Product

Resources

About