GNSS augmentation

About: GNSS augmentation is a research topic. Over the lifetime, 2478 publications have been published within this topic receiving 28513 citations. The topic is also known as: SBAS & Satellite Based Augmentation System.

GNSS method and receiver with camera aid

Abstract: A method of computing navigation information in a portable device comprising a GNSS receiver arranged to receive navigation signals from a plurality of navigation satellites, and a GNSS processor to extract a GNSS position information from said navigation signals. The GNSS receiver is also arranged to obtain image data from a camera module. The method comprises the steps of: deriving a set of position information from the camera module; providing the camera-derived position information to a navigation processor; using said set of camera-derived position information and said navigation signals, to generate a position fix of the GNSS receiver; computing navigation information based on said generated position fix.

P-RANSAC: An Integrity Monitoring Approach for GNSS Signal Degraded Scenario

Abstract: Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an accurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work properly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers. This work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision tasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on subsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this work, a modification to the original RANSAC method is proposed and an analysis of its performance is conducted, processing data collected in a static test.

GNSS integration with vision-based navigation for low GNSS visibility conditions

Abstract: In urban canyons, buildings and other structures often block the line of sight of visible Global Navigation Satellite System (GNSS) satellites, which makes it difficult to obtain four or more satellites to provide a three-dimensional navigation solution. Previous studies on this operational environment have been conducted based on the assumption that GNSS is not available. However, a limited number of satellites can be used with other sensor measurements, although the number is insufficient to derive a navigation solution. The limited number of GNSS measurements can be integrated with vision-based navigation to correct navigation errors. We propose an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GNSS measurements. An integrated model was designed to apply the GNSS range and range rate to vision-based navigation. The possibility of improved navigation performance was evaluated during an observability analysis based on available satellites. According to the observability analysis, each additional satellite decreased the number of unobservable states by one, while vision-based navigation always has three unobservable states. A computer simulation was conducted to verify the improvement in the navigation performance by analyzing the estimated position, which depended on the number of available satellites; additionally, an experimental test was conducted. The results showed that limited GNSS measurements can improve the positioning performance. Thus, our proposed method is expected to improve the positioning performance in urban canyons.

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment

Abstract: This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error.

Loosely-coupled integration of global navigation satellite system and inertial navigation system: speed scale-factor and heading bias calibration

Abstract: Techniques for loosely coupling a Global Navigation Satellite System (“GNSS”) and an Inertial Navigation System (“INS”) integration are disclosed herein. A system includes a GNSS receiver, an INS, and an integration filter coupled to the GNSS receiver and the INS. The GNSS receiver is configured to provide GNSS navigation information comprising GNSS receiver position and/or velocity estimates. The INS is configured to provide INS navigation information based on an inertial sensor output. The integration filter is configured to provide blended position information comprising a blended position estimate and/or a blended velocity estimate by combining the GNSS navigation information and the INS navigation information, and to estimate and compensate at least one of a speed scale-factor and a heading bias of the INS navigation information.