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 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.

Applications of GPS Technologies to Field Sports

2011 ◽  
Vol 6 (3) ◽  
pp. 295-310 ◽  
Author(s):  
Robert J. Aughey
Keyword(s):  
Inertial Sensor ◽  
Atomic Clock ◽  
Team Sports ◽  
Rugby Union ◽  
Sensor Data ◽  
Battery Life ◽  
Positioning Systems ◽  
Gps Technology ◽  
Global Positioning ◽  
Field Sports

Global positioning system (GPS) technology was made possible after the invention of the atomic clock. The first suggestion that GPS could be used to assess the physical activity of humans followed some 40 y later. There was a rapid uptake of GPS technology, with the literature concentrating on validation studies and the measurement of steady-state movement. The first attempts were made to validate GPS for field sport applications in 2006. While GPS has been validated for applications for team sports, some doubts continue to exist on the appropriateness of GPS for measuring short high-velocity movements. Thus, GPS has been applied extensively in Australian football, cricket, hockey, rugby union and league, and soccer. There is extensive information on the activity profile of athletes from field sports in the literature stemming from GPS, and this includes total distance covered by players and distance in velocity bands. Global positioning systems have also been applied to detect fatigue in matches, identify periods of most intense play, different activity profiles by position, competition level, and sport. More recent research has integrated GPS data with the physical capacity or fitness test score of athletes, game-specific tasks, or tactical or strategic information. The future of GPS analysis will involve further miniaturization of devices, longer battery life, and integration of other inertial sensor data to more effectively quantify the effort of athletes.

Observer Design and Stabilization for a Class of Switched System

2011 ◽  
Vol 422 ◽  
pp. 514-518
Author(s):  
Ming Yuan Shieh ◽  
Juing Shian Chiou
Keyword(s):  
Stability Analysis ◽  
Switched Systems ◽  
Sufficient Conditions ◽  
Stability Theorem ◽  
Switched System ◽  
Observer Design ◽  
Asymptotically Stable ◽  
Lyapunov Stability Theorem ◽  
State Transformation ◽  
Switching Law

This paper presents a novel and general approach, which is based on the Lyapunov stability theorem, to synthesize the observer and stabilization of the switched systems. On stability analysis, we can choose a particular state transformation matrix to transfer the observer-based switched system such that all subsystems can be decomposed into stable and unstable blocks. Some sufficient conditions are derived under a switching law such that the observer-based switched system is asymptotically stable. Finally, an example is presented to illustrate the proposed schemes.

scholarly journals Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5031
Author(s):  
Humayun Khan ◽  
Adrian Clark ◽  
Graeme Woodward ◽  
Robert W. Lindeman
Keyword(s):  
Inertial Sensor ◽  
Vision System ◽  
Tracking System ◽  
Indoor Positioning ◽  
Fiducial Marker ◽  
Measurement Unit ◽  
Positioning System ◽  
Positioning Systems ◽  
Marker Tracking ◽  
Training Exercises

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

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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