Inertial navigation aided by simultaneous loacalization and mapping

Unmanned aerial vehicles technologies are getting smaller and cheaper to use and the challenges of payload limitation in unmanned aerial vehicles are being overcome. Integrated navigation system design requires selection of set of sensors and computation power that provides reliable and accurate navigation parameters (position, velocity and attitude) with high update rates and bandwidth in small and cost effective manner. Many of today’s operational unmanned aerial vehicles navigation systems rely on inertial sensors as a primary measurement source. Inertial Navigation alone however suffers from slow divergence with time. This divergence is often compensated for by employing some additional source of navigation information external to Inertial Navigation. From the 1990’s to the present day Global Positioning System has been the dominant navigation aid for Inertial Navigation. In a number of scenarios, Global Positioning System measurements may be completely unavailable or they simply may not be precise (or reliable) enough to be used to adequately update the Inertial Navigation hence alternative methods have seen great attention. Aiding Inertial Navigation with vision sensors has been the favoured solution over the past several years. Inertial and vision sensors with their complementary characteristics have the potential to answer the requirements for reliable and accurate navigation parameters. In this thesis we address Inertial Navigation position divergence. The information for updating the position comes from combination of vision and motion. When using such a combination many of the difficulties of the vision sensors (relative depth, geometry and size of objects, image blur and etc.) can be circumvented. Motion grants the vision sensors with many cues that can help better to acquire information about the environment, for instance creating a precise map of the environment and localize within the environment. We propose changes to the Simultaneous Localization and Mapping augmented state vector in order to take repeated measurements of the map point. We show that these repeated measurements with certain manoeuvres (motion) around or by the map point are crucial for constraining the Inertial Navigation position divergence (bounded estimation error) while manoeuvring in vicinity of the map point. This eliminates some of the uncertainty of the map point estimates i.e. it reduces the covariance of the map points estimates. This concept brings different parameterization (feature initialisation) of the map points in Simultaneous Localization and Mapping and we refer to it as concept of aiding Inertial Navigation by Simultaneous Localization and Mapping. We show that making such an integrated navigation system requires coordination with the guidance and control measurements and the vehicle task itself for performing the required vehicle manoeuvres (motion) and achieving better navigation accuracy. This fact brings new challenges to the practical design of these modern jam proof Global Positioning System free autonomous navigation systems. Further to the concept of aiding Inertial Navigation by Simultaneous Localization and Mapping we have investigated how a bearing only sensor such as single camera can be used for aiding Inertial Navigation. The results of the concept of Inertial Navigation aided by Simultaneous Localization and Mapping were used. New parameterization of the map point in Bearing Only Simultaneous Localization and Mapping is proposed. Because of the number of significant problems that appear when implementing the Extended Kalman Filter in Inertial Navigation aided by Bearing Only Simultaneous Localization and Mapping other algorithms such as Iterated Extended Kalman Filter, Unscented Kalman Filter and Particle Filters were implemented. From the results obtained, the conclusion can be drawn that the nonlinear filters should be the choice of estimators for this application

An Improved FastSLAM System Based on Distributed Structure for Autonomous Robot Navigation

Fast simultaneous localization and mapping (FastSLAM) is an efficient algorithm for autonomous navigation of mobile vehicle. However, FastSLAM must reconfigure the entire vehicle state equation when the feature points change, which causes an exponential growth in quantities of computation and difficulties in isolating potential faults. In order to overcome these limitations, an improved FastSLAM, based on the distributed structure, is developed in this paper. There are two state estimation parts designed in this improved FastSLAM. Firstly, a distributed unscented particle filter is used to avoid reconfiguring the entire system equation in the vehicle state estimation part. Secondly, in the landmarks estimation part, the observation model is designed as a linear one to update the landmarks states by using the linear observation errors. Then, the convergence of the proposed and improved FastSLAM algorithm is given in the sense of mean square. Finally, the simulation results show that the proposed distributed algorithm could reduce the computational complexity with high accuracy and high fault-tolerance performance

Tracking and Estimation Algorithms for Bearings Only Measurements

The Bearings-only tracking problem is to estimate the state of a moving object from noisy observations of its direction relative to a sensor. The Kalman filter, which provides least squares estimates for linear Gaussian filtering problems is not directly applicable because of the highly nonlinear measurement function of the state, representing the bearings measurements and so other types of filters must be considered. The shifted Rayleigh filter (SRF) is a highly effective moment-matching bearings-only tracking algorithm which has been shown, in 2D, to achieve the accuracy of computationally demanding particle filters in situations where the well-known extended Kalman filter and unscented Kalman filter often fail. This thesis has two principal aims. The first is to develop accurate and computationally efficient algorithms for bearings-only tracking in 3D space. We propose algorithms based on the SRF, that allow tracking, in the presence of clutter, of both nonmaneuvering and maneuvering targets. Their performances are assessed, in relation to competing methods, in highly challenging tracking scenarios, where they are shown to match the accuracy of high-order sophisticated particle filters, at a fraction of the computational cost. The second is to design accurate and consistent algorithms for bearings-only simultaneous localization and mapping (SLAM). The difficulty of this problem, originating from the uncertainty in the position and orientation of the sensor, and the absence of range information of observed landmarks, motivates the use of advanced bearings-only tracking algorithms. We propose the quadrature-SRF SLAM algorithm, which is a moment-matching filter based on the SRF, that numerically evaluates the exact mean and covariance of the posterior. Simulations illustrate the accuracy and consistency of its estimates in a situation where a widely used moment-matching algorithm fails to produce consistent estimates. We also propose a Rao-Blackwellized SRF implementation of a particle filter, which, however, does not exhibit favorable consistency properties

The Estimation Methods for an Integrated INS/GPS UXO Geolocation System

This work was supported by a project funded by the US Army Corps of Engineers, Strategic Environment Research and Development Program, contract number W912HQ- 08-C-0044.This report was also submitted to the Graduate School of the Ohio State University in partial fulfillment of the PhD degree in Geodetic Science.Unexploded ordnance (UXO) is the explosive weapons such as mines, bombs, bullets, shells and grenades that failed to explode when they were employed. In North America, especially in the US, the UXO is the result of weapon system testing and troop training by the DOD. The traditional UXO detection method employs metal detectors which measure distorted signals of local magnetic fields. Based on detected magnetic signals, holes are dug to remove buried UXO. However, the detection and remediation of UXO contaminated sites using the traditional methods are extremely inefficient in that it is difficult to distinguish the buried UXO from the noise of geologic magnetic sources or anthropic clutter items. The reliable discrimination performance of UXO detection system depends on the employed sensor technology as well as on the data processing methods that invert the collected data to infer the UXO. The detection systems require very accurate positioning (or geolocation) of the detection units to detect and discriminate the candidate UXO from the non-hazardous clutter, greater position and orientation precision because the inversion of magnetic or EMI data relies on their precise relative locations, orientation, and depth. The requirements of position accuracy for MEC geolocation and characterization using typical state-of-the-art detection instrumentation are classified according to levels of accuracy outlined in: the screening level with position tolerance of 0.5 m (as standard deviation), area mapping (less than 0.05 m), and characterize and discriminate level of accuracy (less than 0.02m). The primary geolocation system is considered as a dual-frequency GPS integrated with a three dimensional inertial measurement unit (IMU); INS/GPS system. Selecting the appropriate estimation method has been the key problem to obtain highly precise geolocation of INS/GPS system for the UXO detection performance in dynamic environments. For this purpose, the Extended Kalman Filter (EKF) has been used as the conventional algorithm for the optimal integration of INS/GPS system. However, the newly introduced non-linear based filters can deal with the non-linear nature of the positioning dynamics as well as the non-Gaussian statistics for the instrument errors, and the non-linear based estimation methods (filtering/smoothing) have been developed and proposed. Therefore, this study focused on the optimal estimation methods for the highly precise geolocation of INS/GPS system using simulations and analyses of two Laboratory tests (cart-based and handheld geolocation system). First, the non-linear based filters (UKF and UKF) have been shown to yield superior performance than the EKF in various specific simulation tests which are designed similar to the UXO geolocation environment (highly dynamic and small area). The UKF yields 50% improvement in the position accuracy over the EKF particularly in the curved sections (medium-grade IMUs case). The UKF also performed significantly better than EKF and shows comparable improvement over the UKF when the IMU noise probability iii density function is symmetric and non-symmetric. Also, since the UXO detection survey does not require the real-time operations, each of the developed filters was modified to accommodate the standard Rauch-Tung-Striebel (RTS) smoothing algorithms. The smoothing methods are applied to the typical UXO detection trajectory; the position error was reduced significantly using a minimal number of control points. Finally, these simulation tests confirmed that tactical-grade IMUs (e.g. HG1700 or HG1900) are required to bridge gaps of high-accuracy ranging solution systems longer than 1 second. Second, these result of the simulation tests were validated from the laboratory tests using navigation-grade and medium-grade accuracy IMUs. To overcome inaccurate a priori knowledge of process noise of the system, the adaptive filtering methods have been applied to the EKF and UKF and they are called the AEKS and AUKS. The neural network aided adaptive nonlinear filtering/smoothing methods (NN-EKS and NN-UKS) which are augmented with RTS smoothing method were compared with the AEKS and AUKS. Each neural network-aided, adaptive filter/smoother improved the position accuracy in both straight and curved sections. The navigation grade IMU (H764G) can achieve the area mapping level of accuracy when the gap of control points is about 8 seconds. The medium grade IMUs (HG1700 and HG1900) with NN-AUKS can maintain less than 10cm under the same conditions as above. Also, the neural network aiding can decrease the difference of position error between the straight and the curved section. Third, in the previous simulation test, the UPF performed better than the other filters. However since the UPF needs a large number of samples to represent the a posteriori statistics in high-dimensional space, the RBPF can be used as an alternative to avoid the inefficiency of particle filter. The RBPF is tailored to precise geolocation for UXO detection using IMU/GPS system and yielded improved estimation results with a small number of samples. The handheld geolocation system using HG1900 with a nonlinear filter-based smoother can achieve the discrimination level of accuracy if the update rate of control points is less than 0.5Hz and 1Hz for the sweep and swing respectively. Also, the sweep operation is more preferred than the swing motion because the position accuracy of the sweep test was better than that of the swing test

On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables’ statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization