GPS/INS

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

An accurate localization for mobile robot using extended Kalman filter and sensor fusion

Abstract: This paper presents an accurate localization scheme for mobile robots based on the fusion of an ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve an accuracy of less than 100 mm. The INS consists of a yaw gyro and two wheel-encoders, and the U-SAT consists of four transmitters and a receiver. Besides the proposed localization method, we will fuse these in an extended Kalman filter. The performance of the localization was verified by simulation and two actual data sets (straight and curve) gathered from about 0.5 m/s of actual driving data. The localization methods used were general sensor fusion and sensor fusion through a Kalman filter using data from the INS. Through simulation and actual data analysis, the experiment shows the effectiveness of the proposed method for autonomous mobile robots.

System and method for advanced tight coupling of GPS and inertial navigation sensors

Abstract: A system and method for generating a navigation solution in high interference and dynamic environments using Global Positioning System (GPS) and inertial measurement unit (IMU) data is described. The system includes an Advanced Tightly Coupled (ATC) tracking processor for a multi-satellite tracking system. The ATC accepts early, late, and on-time I and Q data from the GPS signal tracker and outputs vehicle-to-satellite range and range rate residual measurements to a navigation Kalman filter. The ATC includes nonlinear discriminators which transform I and Q data into linear residual measurements corrupted by unbiased, additive, and white noise. It also includes an amplitude estimator configured to operate in rapidly changing, high power noise; a measurement noise variance estimator; and a linear residual smoothing filter for input to the navigation filter.

Comparison of low-cost GPS/INS sensors for Autonomous Vehicle applications

Abstract: Autonomous Vehicle applications (Unmanned Ground Vehicles, Micro-Air Vehicles, UAVpsilas, and Marine Surface Vehicles) all require accurate position and attitude to be effective. Commercial units range in both cost and accuracy, as well as power, size, and weight. With the advent of low-cost blended GPS/INS solutions, several new options are available to accomplish the positioning task. In this work, we experimentally compare three commercially available, off-the-shelf units insitu, in terms of both position, and attitude. The compared units are a Microbotics MIDG-II, a Tokimec VSAS-2GM, along with a KVH Fiber Optic Gyro. The position truth measure is from a Trimble Ag122 DGPS receiver, and the attitude truth is from the KVH in yaw. Care is taken to make sure that all measurements are taken simultaneously, and that the sensors are all mounted rigidly to the vehicle chassis. A series of measurement trials are performed, including light driving on coastal roads and highway speeds, static bench testing, and flight data taken in a light aircraft both flying up the coast as well as aggressively maneuvering. Allan Variance analysis performed on all of the sensors, and their noise characteristics are compared directly. A table is included with the final consistent models for these sensors, and a methodology for creating such models for any additional sensors as they are made available. The Microbotics MIDG-II demonstrates performance that is superior to the Tokimec VSAS-2GM, both in terms of raw positioning data, as well as attitude data. While both perform quite well during flight, the MIDG is much better during driving tests. This is due to the MIDG internal tightly-coupled architecture, which is able to better fuse the GPS information with the noisy inertial sensor measurements.

Vehicle positioning by map matching as feedback for ins/gps navigation system during gps signal loss

Abstract: A method and apparatus of vehicle positioning uses map matching as feedback for an integrated navigation system where a map and navigation system is coupled with an inertial navigation system (INS) using low-precision vehicle sensors when the vehicle passes through a tunnel or other area suffering from GPS signal loss. The method and apparatus operates to detect whether the vehicle has reached an entry point of a tunnel, and if so, immediately starts a map matching operation to match the current vehicle position with a road link of the tunnel. The current position determined by the map matching operation is feedbacked to an integration Kalman filter thereby correcting errors caused by the vehicle sensors. The method and apparatus resumes the normal navigation operation including GPS navigation as soon as it detects that the vehicle is out of the tunnel.

Location tracking in GPS using Kalman Filter through SMS

Abstract: We present a novel technique to send GPS coordinates to other mobiles through Short Message Service (SMS) based on Global Positioning System (GPS) technology. Two algorithms, Kalman Filter and Velocity Renovation, which can be used in conjunction with GPS are used as a basis for location tracking.