The positioning accuracy of global navigation satellite systems (GNSS) in dense urban canyon environment significantly deteriorates due to multipath and signal blockage. For this reason, inertial navigation system (INS) is often integrated with GNSS to ensure a reliable navigation solution during such periods of GNSS signal outages. A low-cost navigation solution for land vehicles has been developed by integrating GNSS positioning solution with the measurements from the vehicle motion sensors (accelerometers and gyroscopes). The major drawback of the usage of these inertial sensors is its progressive error accumulation, where the gyroscope drift errors increase gradually, leading to an unusable position estimate, especially in the absence of GNSS updates. Navigation in GNSS-denied environment requires aiding INS with other exteroceptive sensors such as cameras to guarantee the continuity of reliable positioning updates. The estimation of the camera’s relative change in position and orientation over time is known as visual odometry (VO). A VO-based multisensor integrated navigation system is presented here to surmount the inaccuracy of GNSS in urban scenarios and the drifts of the motion sensors. To enhance the overall system accuracy of the VO-based integrated solution, this paper explores improving the positioning accuracy during GNSS outages by nonlinear modeling of the residual position errors using a neural network. The results show a significant accuracy improvement over relatively long GNSS outages.
KEYWORDS: LIDAR, Unmanned aerial vehicles, Data modeling, Airborne laser technology, Satellite navigation systems, Systems modeling, Laser scanners, Real time processing algorithms, Real time avionics simulations, Real time imaging
During the last two decades, several research papers have addressed robust filtering algorithms for the airborne laser scanning (ALS) data. Although most of these filtering algorithms are accurate and robust, they are limited to postprocessing since they rely on complex algorithms and needs high execution time if implemented in an embedded processor. There are number of applications that require generating digital surface models (DSMs) in real-time such as path planning for ground vehicles, where a UAV equipped with a LiDAR scan the terrain ahead to find the path ahead of a ground vehicle. LiDAR scans are also critical to assist with finding the most suitable region for UAV Landing. With the growing demand for safe operation of autonomous systems like UAVs, there is a need for efficient LiDAR processing algorithms capable of generating DSMs in real-time. The aim of this research is to discuss the design of an efficient algorithm that can filter LiDAR point cloud, generate DSM and operate in real-time. The algorithm is suitable for real-time implementation on limited resources embeddedprocessors without the need for a supercomputer. It is also capable of estimating the slope maps from the DSM. The proposed method was successfully implemented in C++ in real-time and was examined in an airborne platform. With comparison to the reference data, we were able to demonstrate the capability of the developed method to distinguish, in real-time, the roofs of the buildings (areas of low slope) from the edges of the same buildings (areas of high slope).
Inertial navigation systems (INS) incorporating three mutually orthogonal accelerometers and three mutually orthogonal gyroscopes are integrated with global positioning systems (GPS) to provide reliable and accurate positioning information for vehicular navigation. Because of their high reliability and accuracy, ring laser gyroscopes (RLG) and fiber optic gyroscopes (FOG) are usually utilized inside most of the present INS. However, bias drift at the output of these optical gyroscopes may deteriorate the performance of the overall INS/GPS navigation system. This paper introduces a method to enhance the performance of optical gyros in two phases. The first phase utilizes wavelet multi-resolution analysis to band limit the gyro measurement and improves its signal-to-noise ratio. The second phase employs radial-basis function (RBF) neural networks to predict drift errors. The drift model provided by the RBF network is established using the gyro raw measurement and time as inputs and provides the drift error at its output. The RBF neural networks are utilized in this study since they generally have simpler architecture and faster training procedure than other neural network types. The proposed method is applied to E-core 2000 FOG (KVH Industries Inc., Rhode Island, USA).
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