scholarly journals FloorVLoc: A Modular Approach to Floorplan Monocular Localization

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 69
Author(s):  
John Noonan ◽  
Ehud Rivlin ◽  
Hector Rotstein
Keyword(s):  
Linear Optimization ◽  
Intelligent Vehicles ◽  
Average Error ◽  
Vehicle Localization ◽  
Positioning Systems ◽  
Monocular Camera ◽  
Accurate Localization ◽  
Emergency Personnel ◽  
Localization And Mapping ◽  
Global Positioning

Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

scholarly journals Research Patterns and Trends in Localization of IoVs

2019 ◽  
Vol 9 (1S5) ◽  
pp. 317-321
Keyword(s):  
Vehicular Networks ◽  
High Accuracy ◽  
Security And Privacy ◽  
Review Of Literature ◽  
Global Positioning Systems ◽  
Positioning Systems ◽  
Accurate Localization ◽  
Adhoc Network ◽  
Global Positioning ◽  
Work Done

The accurate localization of Internet of Vehicle (IoV) is essential for promoting safety on roads. IoVs are evolving Vehicular Adhoc NETwork (VANETs). The objective is to automate various security aspects and efficiency features in vehicular networks. In this study, we conduct a review of literature and investigate the techniques used for localization of IoVs on roads. This study identifies major issues occurring in localization of IoVs using Global Positioning Systems (GPS). The major challenges are; 1) To achieve high accuracy in localization. 2) To obtain Error free localization of IoVs. 3) Verification of location of IoVs. 4) Security and privacy of vehicle. In order to develop robust IoVs, these issues are to be addressed efficiently. Various researchers have made the contribution by developing numerous algorithms and techniques. This paper reviews the techniques being deployed to overcome the challenges and reports the trends and patterns already set in the field of localization of IoVs. Our paper summarizes the worthy work done by researchers in this field and lays the necessary foundation for the improved implementation of novel and more efficient techniques.

Observer design for monocular visual inertial SLAM

2018 ◽  
Vol 66 (3) ◽  
pp. 246-257
Author(s):  
Geoff Fink ◽  
Mirko Franke ◽  
Alan F. Lynch ◽  
Klaus Röbenack
Keyword(s):  
Linear Time ◽  
Inertial Sensor ◽  
Vision System ◽  
Sufficient Conditions ◽  
Observer Design ◽  
State Transformation ◽  
Positioning Systems ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Sufficient Conditions For Convergence

Abstract This paper examines the state estimation problem for unmanned aerial vehicles when commonly used positioning systems such as the global positioning system or indoor motion capture systems are unavailable. The proposed method uses inertial sensor measurements along with scaled position measurements from an onboard computer vision system which implements visual simultaneous localization and mapping. A state transformation puts the system into a linear time-varying form which simplifies observability analysis and allows for an observer design with sufficient conditions for convergence. The proposed design is validated by simulation.

scholarly journals GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment

2016 ◽  
Vol 11 (1) ◽  
pp. 62 ◽  
Author(s):  
Mohammed Aftatah ◽  
Abdelkabir Lahrech ◽  
Abdelouahed Abounada ◽  
Aziz Soulhi
Keyword(s):  
Urban Areas ◽  
Inertial Sensor ◽  
Real Data ◽  
Vehicle Localization ◽  
Land Vehicle ◽  
Accurate Localization ◽  
Global Positioning ◽  
Proprioceptive Sensors ◽  
Sensor Errors ◽  
Fusion Approach

The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.

Improved intelligent vehicle self-localization with integration of sparse visual map and high-speed pavement visual odometry

2020 ◽  
Vol 235 (1) ◽  
pp. 177-187
Author(s):  
Gang Huang ◽  
Zhaozheng Hu ◽  
Qianwen Tao ◽  
Fan Zhang ◽  
Zhe Zhou
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Visual Odometry ◽  
Lateral View ◽  
Intelligent Vehicles ◽  
High Speed Camera ◽  
Vehicle Localization ◽  
Localization And Mapping ◽  
Resolution Imaging ◽  
Fundamental Requirement

Localization is a fundamental requirement for intelligent vehicles. Conventional localization methods usually suffer from various limitations, such as low accuracy and blocked areas for Global Positioning System, high cost for inertial navigation system or light detection and ranging, and low robustness for visual simultaneous localization and mapping or visual odometry. To overcome these problems, we propose a novel localization method integrated with a sparse visual map and a high-speed pavement visual odometry. We use a lateral-view camera to sense the sparse visual map node for accurate map-based localization. We use a down-view high-speed camera for odometry computation between two sparse visual map nodes. With a high-speed camera, it is possible to extract and track pavement features with stable resolution imaging even in high-speed movement. We also develop a data-driven motion model for the Kalman filter to fuse the localization results from the sparse map and the high-speed pavement visual odometry to enhance vehicle localization. The proposed method was tested in two different scenarios in different pavement conditions. The experimental results demonstrate that the proposed method can improve vehicle localization with low cost and high feasibility.

Confiabilidad de los registros obtenidos por medio de dos sistemas de posicionamiento global en actividades de desplazamiento humano

2012 ◽  
Vol 2 (3 - 4) ◽  
pp. 117
Author(s):  
Jeison Daniel Salazar Pachón ◽  
David Armando Chaparro Obando ◽  
Nicolás Tordi
Keyword(s):  
Global Positioning Systems ◽  
Positioning Systems ◽  
Global Positioning ◽  
El Sistema

<p>El presente estudio examinó  la confiabilidad de los registros de dos sistemas de posicionamiento global (<em>global positioning systems  </em>[GPS]), Garmin310XT y FRWDB600,  sobre  las distancias  recorridas a diferentes  velocidades,  tras un protocolo a pie y otro  en bicicleta realizados  en una pista atlética.  Esta información se comparó con el trayecto  real de recorrido, hecho a partir  del cálculo: <em>ritmo de recorrido (r) = distancia recorrida (d) x tiempo  de recorri- do, </em>y se controló con un metrónomo Sport Beeper. Los participantes fueron dos jóvenes de edad  media  22 años  ± 1, activos  físicamente. En los resultados, se observaron diferencias  entre los registros de ambos sistemas GPS; el protocolo a pie Garmin tuvo un porcentaje de concordancia de 101,1%, mientras que FRWD presentó  103%. En el protocolo en bicicleta se obtuvo 103,4% y 101,6%, respectivamente. Se concluyó  que el uso de GPS es más fiable cuando  las velocidades  de desplazamiento humano son bajas  o moderadas  para  el sistema Garmin  (7-14 km/h), ya que al ser más altas la fiabilidad  de la información podría  ser menor, mientras  que el sistema FRWD presentó  mayor confiabilidad en velocidades moderadas (14-22 km/h).</p>

Confiabilidad de los registros obtenidos por medio de dos sistemas de posicionamiento global en actividades de desplazamiento humano

2011 ◽  
Vol 1 (2) ◽  
pp. 117
Author(s):  
Jeison Daniel Salazar Pachón ◽  
David Armando Chaparro Obando ◽  
Nicolás Tordi
Keyword(s):  
Global Positioning Systems ◽  
Positioning Systems ◽  
Global Positioning ◽  
El Sistema

El presente estudio examinó  la confiabilidad de los registros de dos sistemas de posicionamiento global (<em>global positioning systems  </em>[GPS]), Garmin310XT y FRWDB600,  sobre  las distancias  recorridas a diferentes  velocidades,  tras un protocolo a pie y otro  en bicicleta realizados  en una pista atlética.  Esta información se comparó con el trayecto  real de recorrido, hecho a partir  del cálculo: <em>ritmo de recorrido (r) = distancia recorrida (d) x tiempo  de recorrido, </em>y se controló con un metrónomo Sport Beeper. Los participantes fueron dos jóvenes de edad  media  22 años  ± 1, activos  físicamente. En los resultados, se observaron diferencias  entre los registros de ambos sistemas GPS; el protocolo a pie Garmin tuvo un porcentaje de concordancia de 101,1%, mientras  que FRWD presentó  103%. En el protocolo en bicicleta se obtuvo 103,4% y 101,6%, respectivamente. Se concluyó  que el uso de GPS es más fiable cuando  las velocidades  de desplazamiento humano son bajas  o mo- deradas  para  el sistema Garmin  (7-14 km/h), ya que al ser más altas la fiabilidad  de la información podría  ser menor, mientras  que el sistema FRWD presentó  mayor confiabilidad en velocidades moderadas (14-22 km/h).

scholarly journals A Time-Based Objective Measure of Exposure to the Food Environment

2019 ◽  
Vol 16 (7) ◽  
pp. 1180 ◽  
Author(s):  
Jason Scully ◽  
Anne Moudon ◽  
Philip Hurvitz ◽  
Anju Aggarwal ◽  
Adam Drewnowski
Keyword(s):  
Food Environment ◽  
Fast Food ◽  
Objective Measure ◽  
Mobility Patterns ◽  
Time Duration ◽  
Positioning Systems ◽  
Global Positioning ◽  
Fast Food Restaurants ◽  
Logistic Regressions ◽  
The Relationship

Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

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