Design and Implementation of Bio-inspired Underwater Electrosense

Underwater electrosense, manipulating underwater electric field for sensing purpose, is a growing technology bio-inspired by weakly electric fish that can navigate in dark or cluttered water. We studied its theoretical foundations and developed sophisticated sensing algorithms including some first-introduced techniques such as discrete dipole approximation (DDA) and convolutional neural networks (CNN), which were tested and validated by simulation and a planar sensor prototype. This work pave a solid way to applications on practical underwater robots

Optimal state estimation for cavity optomechanical systems

We demonstrate optimal state estimation for a cavity optomechanical system through Kalman filtering. By taking into account nontrivial experimental noise sources, such as colored laser noise and spurious mechanical modes, we implement a realistic state-space model. This allows us to obtain the conditional system state, i.e., conditioned on previous measurements, with minimal least-square estimation error. We apply this method for estimating the mechanical state, as well as optomechanical correlations both in the weak and strong coupling regime. The application of the Kalman filter is an important next step for achieving real-time optimal (classical and quantum) control of cavity optomechanical systems.Comment: replaced with published version, 5+12 page

Polarimetric Enhancements to Electro-Optical Aided Navigation Techniques

Navigation in indoor and urban environments by small unmanned systems is a topic of interest for the Air Force. The Advanced Navigation Technology Center at the Air Force Institute of Technology is continually looking for novel approaches to navigation in GPS deprived environments. Inertial sensors have been coupled with image aided concepts, such as feature tracking, with good results. However, feature density in areas with large, flat, smooth surfaces tends to be low. Polarimetric sensors have been used for surface reconstruction, surface characterization and outdoor navigation. This thesis combines aspects of some of these algorithms along with a realistic, micro-facet polarimetric model and a Kalman filter approach to determine surface structure and platform orientation in an indoor environment. A series of graphical user interfaces were developed to estimate surface material parameters. A more complex simulation software package was used to estimate camera attitude. A physical polarimeter was also designed and built to test the algorithms in a realistic environment. An improvement in attitude estimation of up to 50% was demonstrated

OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES

Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic operations of submerged vehicles in dynamic aquatic environments. However, underwater perception encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and object perception techniques for underwater robots in different environmental conditions. Due to the current sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range, medium-range, and long-range. We survey and describe recent advances for each perception range and suggest some potential future research directions worthy of investigating in this field

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

Nonstationary shape estimation in electrical impedance tomography using a parametric level set-based extended Kalman filter approach

This paper presents a parametric level set based reconstruction method for non-stationary applications using electrical impedance tomography (EIT). Owing to relatively low signal to noise ratios in EIT measurement systems and the diffusive nature of EIT, reconstructed images often suffer from low spatial resolution. In addressing these challenges, we propose a computationally efficient shape-estimation approach where the conductivity distribution to be reconstructed is assumed to be piecewise constant, and the region boundaries are assumed to be non-stationary in the sense that the characteristics of region boundaries change during measurement time. The EIT inverse problem is formulated as a state estimation problem in which the system is modeled with a state equation and an observation equation. Given the temporal evolution model of the boundaries and observation model, the objective is to estimate a sequence of states for the nonstationary region boundaries. The implementation of the approach is based on the finite element method and a parametric representation of the region boundaries using level set functions. The performance of the proposed approach is evaluated with simulated examples of thorax imaging, using noisy synthetic data and experimental data from a laboratory setting. In addition, robustness studies of the approach w.r.t the modeling errors caused by inaccurately known boundary shape, non-homogeneous background and varying conductivity values of the targets are carried out and it is found that the proposed approach tolerates such kind of modeling errors, leading to good reconstructions in non-stationary situations