GPS/INS

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

Lidar and ins fusion in periods of gps outages for mobile laser scanning mapping systems

Abstract: Mobile laser scanning systems are becoming an increasingly popular means to obtain 3D coverage on a large scale. To perform the mapping, the exact position of the vehicle must be known throughout the trajectory. Exact position is achieved via integration of Global Positioning Systems (GPS) and Inertial Navigation Systems (INS). Yet, in urban environments, cases of complete or even partial GPS outages may occur leaving the navigation solution to rely only on the INS. The INS navigation solution degrades with time as the Inertial Measurement Unit (IMU) measurements contains noise, which permeates into the navigation equations. Degradation of the position determination leads to loss of data in such segments. To circumvent such drift and its effects, we propose fusing INS with lidar data by using building edges. This detection of edges is then translated into position data, which is used as an aiding to the INS. It thereby enables the determination of the vehicle position with a satisfactory level accuracy, sufficient to perform the laser-scanning based mapping in those outage periods.

Gyroscope Drift Estimation in Tightly-coupled INS/GPS Navigation System

Abstract: The objective of this paper is to analyze the estimation performance of gyroscope drifts in INS/GPS integrated navigation systems, while system is in motion. Strapdown inertial navigation system (SINS) error model is augmented by inertial sensor bias and drift and GPS clock bias and drift to improve the integration performance. To estimate the augmented state error, instead of GPS position, GPS pseudoranges and pseudorange rates are used. In tightly-coupled integration GPS and INS under different motion conditions, it is observed that the vertical gyro drift estimation is very weak in all cases of stationary, linear motion, and three-axis sway motion via Kalman filter covariance analysis. In order to solve the vertical gyroscope drift estimation problem, zero update velocity (ZUPT) algorithm is proposed to improve the observability of vertical gyro drift. Computer simulation is carried out by this algorithm to validate the feasibility of the concept. The simulation results demonstrate that vertical gyro drift can be better estimated for the sake of compensation afterward.

Trajectory estimation improvement based on time-series constraint of GPS Doppler and INS in urban areas

Abstract: With the development of various driver assistance systems using vehicle position, improvement of positioning accuracy is desired. GPS, widely used as a positioning system, can provide accuracy of a few meters in suburban areas. However, in urban areas, this accuracy can be degraded to ten meters or more because of the reflection and blocking of GPS signals by tall buildings. In order to maintain accurate positioning in such an environment, it is important to develop a method of trajectory estimation which is robust against the surrounding buildings. In this paper, the heading estimation which is the most influential factor in trajectory accuracy is improved by tight coupling of time-series GPS-Doppler and INS. As a result, the accuracy of trajectory estimation is improved in urban areas.

Intelligently tuned wavelet parameters for GPS/INS error estimation

Abstract: This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule (TSR), and the optimum level of decomposition (LOD). This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System

Abstract: Presently, Kalman filter (KF) is used to fuse data from both inertial navigation systems (INS) and global positioning systems (GPS) to provide position, velocity and attitude information. However, several drawbacks associated with KF like its immunity to noise, its dependency on predefined errors models, has encouraged research activates towards investigation of other integration techniques. This study proposes and discusses the real-time implementation of adaptive neuro-fuzzy inference system (ANFIS) to fuse GPS and INS data for vehicular navigation applications. The proposed method was examined and compared to KF when applied to Ashtech Z12 GPS receiver and a navigation-grade INS (Honeywell LRF-III) that have been utilized inside a land vehicle. The system is evaluated while considering several intentionally introduced GPS outages for periods of 20 seconds. The ANFIS-based navigation system was able to provide the vehicle position with errors, which were mostly below 2 m. The experimental results demonstrated the advantages of the proposed AI-based INS/GPS integration techniques in regards of robustness while ensuring system position accuracy in real-time.