Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs sub- jected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Airborne vision-based attitude estimation and localisation

Vision plays an integral part in a pilot's ability to navigate and control an aircraft. Therefore Visual Flight Rules have been developed around the pilot's ability to see the environment outside of the cockpit in order to control the attitude of the aircraft, to navigate and to avoid obstacles. The automation of these processes using a vision system could greatly increase the reliability and autonomy of unmanned aircraft and flight automation systems. This thesis investigates the development and implementation of a robust vision system which fuses inertial information with visual information in a probabilistic framework with the aim of aircraft navigation. The horizon appearance is a strong visual indicator of the attitude of the aircraft. This leads to the first research area of this thesis, visual horizon attitude determination. An image processing method was developed to provide high performance horizon detection and extraction from camera imagery. A number of horizon models were developed to link the detected horizon to the attitude of the aircraft with varying degrees of accuracy. The second area investigated in this thesis was visual localisation of the aircraft. A terrain-aided horizon model was developed to estimate the position, altitude as well as attitude of the aircraft. This gives rough positions estimates with highly accurate attitude information. The visual localisation accuracy was improved by incorporating ground feature-based map-aided navigation. Road intersections were detected using a developed image processing algorithm and then they were matched to a database to provide positional information. The developed vision system show comparable performance to other non-vision-based systems while removing the dependence on external systems for navigation. The vision system and techniques developed in this thesis helps to increase the autonomy of unmanned aircraft and flight automation systems for manned flight

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Ultrasonic and IMU based high precision UAV localisation for the low cost autonomous inspection in oil and gas pressure vessels

With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position

Adaptive extended Kalman filter based fusion approach for high precision UAV positioning in extremely confined environments

For unmanned aerial vehicle (UAV)-based smart inspection in extremely confined environments, it is impossible for precise UAV positioning with global positioning system, owing to the satellite signal block. Therefore, the ultrawideband (UWB)-based technology has attracted extensive attention under such circumstances. However, due to the unpredictable propagation condition and the time-varying operational environment, the localization performance oscillation caused by the changing measurement noise may lead to the instability of UAV. To mitigate the effects, in this article, a high-precision UAV positioning system which integrates the inertial measurement unit and UWB with the adaptive extended Kalman filter (EKF) is proposed. Compared with the traditional EKF-based approach, the estimated and recorded information from previous processes is exploited to adaptively estimate and further control the estimation of the noise covariance matrices for the performance improvement. Finally, simulations and experiments have been conducted in extremely confined environments. According to the results, the proposed algorithm can significantly improve the position update rate, the median positioning error, the 95 th percentile positioning error, and the average standard deviation into 88 Hz, 0.102 m, 0.192 m, and 0.052 m, which is applicable for applications in focused environments

A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations