GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

Initial Position Estimation Using RFID Tags: A Least-Squares Approach

Abstract: The GPS has revolutionized how people, vehicles, and objects are positioned. The GPS, however, has limitations. It will only work well where a signal can be received and will not work underground, in tunnels, or even some buildings. Obtaining an accurate position estimate in these areas must therefore use alternate methods that do not rely on GPS. Promising research from the field of robotics provides an alternative approach to positioning, using a technique known as simultaneous localization and mapping (SLAM). The challenge for the SLAM algorithm is that the initial position given to the algorithm must be accurate. This paper investigates the concept of using an array of RF identification (RFID) tags placed at known positions to provide the initial position of the stationary vehicle to the SLAM algorithm. A least-squares (LS)-based position estimator is presented and evaluated in an experiment conducted in an underground potash mine and an indoor environment at the University of Saskatchewan. The estimator's average error is calculated using models with a varied number of parameters. It was found that both environments attain the best results with five model parameters that were obtained from data taken in the same environment. The results suggest that RFID-based positioning, using this LS approach, has the potential to provide relatively accurate and low-cost initial position estimation.

Method for monitoring integrity of an integrated GPS and INU system

Abstract: A method for monitoring the integrity of an integrated positioning system located on a mobile machine is provided. The integrated positioning system includes a GPS receiver and an inertial navigation unit (INU). The INU includes an odometer, a Doppler radar, a gyroscope, and a sensor for measuring the steering angle of the mobile machine. The method includes the steps of receiving a GPS position estimate from a GPS receiver, receiving an INU position estimate from an inertial navigation unit, and comparing the GPS position estimate and the INU position estimate. If the two position estimates are the same, then the system is VALID. Otherwise, the velocity of the mobile machine as determined by the odometer and velocity as determined by the Doppler radar are compared. If the difference is greater than a first predetermined threshold, then the INU is determined to be INVALID. Otherwise a heading rate from gyroscope is compared with a calculated heading rate based on measured steering angle and velocity. If the difference is greater than a second predetermined threshold, then the INU is INVALID, otherwise the GPS is INVALID.

Navigating Urban Areas by VISAT—A Mobile Mapping System Integrating GPS/INS/Digital Cameras for GIS Applications

Abstract: Following a brief system description of VISAT (Video, Inertial, and SATellite GPS), a mobile survey system for the establishment of geographic information systems (GIS) in urban centers, the quality control component of the system and results of a performance analysis are discussed. Data collected in test networks in Calgary, Denver, and Laval City are used to assess system performance in terms of accuracy and reliability, and to develop a real-time quality control algorithm for the navigation sensors. Special attention is given to detecting and fixing GPS cycle slips by applying independent inertial navigation system (INS) trajectory data. In urban centers where, because of tree coverage and shading by buildings, frequent cycle slips are the rule rather than the exception, the effectiveness of INS bridging is crucial for overall performance. Results show that system specifications have been met, and in many cases performance is better than specified.

Performance enhancement for ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman filter

Abstract: This paper conducts performance evaluation for the ultra-tight integration of Global positioning system (GPS) and inertial navigation system (INS) by use of the fuzzy adaptive strong tracking unscented Kalman filter (FASTUKF). An ultra-tight GPS/INS integration architecture involves fusion of the in-phase and quadrature components from the correlator of the GPS receiver with the INS data. These two components are highly nonlinearly related to the navigation states. The strong tracking unscented Kalman filter (STUKF) is based on the combination of an unscented Kalman filter (UKF) and strong tracking algorithm (STA) to perform the parameter adaptation task for various dynamic characteristics. The STA is basically a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in a traditional approach for selecting the softening factor through personal experience or computer simulation, the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor, leading to the FASTUKF. Two examples are provided for illustrating the effectiveness of the design and demonstrating effective improvement in navigation estimation accuracy and, therefore, the proposed FASTUKF algorithm can be considered as an alternative approach for designing the ultra tightly coupled GPS/INS integrated navigation system.

Ground vehicle navigation in harsh urban conditions by integrating inertial navigation system, global positioning system, odometer and vision data

Abstract: Combining GPS/INS/odometer data has been considered one of the most attractive methodologies for ground vehicle navigation. In the case of long GPS signal blockages inherent to complex urban environments, however, the accuracy of this approach is largely deteriorated. To overcome this limitation, this study proposes a novel ground vehicle navigation system that combines INS, odometer and omnidirectional vision sensor. Compared to traditional cameras, omnidirectional vision sensors can acquire much more information from the environment thanks to their wide field of view. The proposed system automatically extracts and tracks vanishing points in omnidirectional images to estimate the vehicle rotation. This scheme provides robust navigation information: specifically by combining the advantages of vision, odometer and INS, we estimate the attitude without error accumulation and at a fast running rate. The accurate rotational information is fed back into a Kalman filter to improve the quality of the INS bridging in harsh urban conditions. Extensive experiments have demonstrated that the proposed approach significantly reduces the accumulation of position, velocity and attitude errors during simulated GPS outages. Specifically, the position accuracy is improved by over 30% during simulated GPS outages.