Integration of GPS and vision measurements for navigation in GPS challenged environments

Soloviev, Andrey; Venable, Donald

doi:10.1109/plans.2010.5507322

Cited by 37 publications

(36 citation statements)

References 2 publications

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“…While the cameras are low-priced, the precision of relative positioning using the images is high as compared with other navigation sensors. Furthermore, we can easily obtain image sequences during driving, hybrid approaches using images for positioning also seem to be promising (Soloviev and Venable, 2010;Kim et al, 2011;Yoo et al, 2005, Kim et al, 2004Goldshtein et al, 2007). Single camera or more cameras' images are used with GPS data to enhance the positioning accuracy in many studies.…”

Section: Indrodouctionmentioning

confidence: 99%

“…They performed the fusion the IMU and camera in a tightly coupled manner by an error state extended Kalman filter. Soloviev and Venable (2010) investigated into the feasibility of the combination of GPS and a single video camera for navigation in GPS-challenged environments including tunnel or skyscraper area where GPS signal blockage occurs. They also demonstrated the performance of the method using only simulated data.…”

Section: Indrodouctionmentioning

confidence: 99%

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Improving Car Navigation With a Vision-Based System

Kim

Choi

Lee

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The real-time acquisition of the accurate positions is very important for the proper operations of driver assistance systems or autonomous vehicles. Since the current systems mostly depend on a GPS and map-matching technique, they show poor and unreliable performance in blockage and weak areas of GPS signals. In this study, we propose a vision oriented car navigation method based on sensor fusion with a GPS and in-vehicle sensors. We employed a single photo resection process to derive the position and attitude of the camera and thus those of the car. This image georeferencing results are combined with other sensory data under the sensor fusion framework for more accurate estimation of the positions using an extended Kalman filter. The proposed system estimated the positions with an accuracy of 15 m although GPS signals are not available at all during the entire test drive of 15 minutes. The proposed vision based system can be effectively utilized for the low-cost but high-accurate and reliable navigation systems required for intelligent or autonomous vehicles.

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Section: Indrodouctionmentioning

confidence: 99%

Section: Indrodouctionmentioning

confidence: 99%

Improving Car Navigation With a Vision-Based System

Kim

Choi

Lee

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The fusion is performed in two equations defining the relationship between the device range to features and its pose change and one equation expressing the relationship between the carrier phase and position changes. In [69], these equations are resolved using the least mean square estimate. The experimental results carried out in [69] show that the GPS The fusion of GNSS and monocular SLAM within a BA framework is addressed in [70], through a BA with inequality constraint (IBA).…”

Section: Gnss and Camera Fusionmentioning

confidence: 99%

“…In [69], these equations are resolved using the least mean square estimate. The experimental results carried out in [69] show that the GPS The fusion of GNSS and monocular SLAM within a BA framework is addressed in [70], through a BA with inequality constraint (IBA). The reprojection error is computed based on both the camera and GPS poses.…”

Section: Gnss and Camera Fusionmentioning

confidence: 99%

Review and classification of vision‐based localisation techniques in unknown environments

Ben-Afia

Deambrogio

Salos

et al. 2014

IET Radar, Sonar & Navigation

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International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position

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“…Much prior work in visual SLAM has focused on either eliminating the scale ambiguity, through the inclusion of inertial measurements [1], [2] or GPS carrier-phase measurements [3], or employing previously-mapped visually recognizable markers, referred to as fiduciary markers [4]. In contrast, there has been little prior work that attempts to solve the problem of anchoring the local navigation solution produced by visual SLAM to a global reference frame without the use of an a priori map of the environment, even though the no-prior-maptechnique is preferred or required for many applications.…”

Section: Introductionmentioning

confidence: 99%

High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements

Shepard

Humphreys

2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Abstract-A novel navigation system for obtaining highprecision globally-referenced position and attitude is presented and analyzed. The system is centered on a bundleadjustment-based visual simultaneous localization and mapping (SLAM) algorithm which incorporates carrierphase differential GPS (CDGPS) position measurements into the bundle adjustment in addition to measurements of point features identified in a subset of the camera images, referred to as keyframes. To track the motion of the camera in real-time, a navigation filter is employed which utilizes the point feature measurements from all non-keyframes, the point feature positions estimated by bundle adjustment, and inertial measurements. Simulations have shown that the system obtains centimeter-level or better absolute positioning accuracy and sub-degree-level absolute attitude accuracy in open outdoor areas. Moreover, the position and attitude solution only drifts slightly with the distance traveled when the system transitions to a GPS-denied environment (e.g., when the navigation system is carried indoors). A novel technique for initializing the globallyreferenced bundle adjustment algorithm is also presented which solves the problem of relating the coordinate systems for position estimates based on two disparate sensors while accounting for the distance between the sensors. Simulation results are presented for the globally-referenced bundle adjustment algorithm which demonstrate its performance in the challenging scenario of walking through a hallway where GPS signals are unavailable.

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Integration of GPS and vision measurements for navigation in GPS challenged environments

Cited by 37 publications

References 2 publications

Improving Car Navigation With a Vision-Based System

Improving Car Navigation With a Vision-Based System

Review and classification of vision‐based localisation techniques in unknown environments

High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements

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