Geomagnetic Aided Dead-Reckoning Navigation

The dependence of modern navigation methods on global positioning systems has led to developing alternative algorithms for localization, capable of providing reliable and robust estimations. Global position system is commonly used in a vast majority of the world’s devices, and it can supply real time position and velocity information. However, its accuracy can be compromised by external operational effects such as signal availability, cyber-attacks or weather conditions. This thesis investigates an alternative approach to enhance navigation in GPS-denied environments. Particularly, it develops an integrated navigation architecture based on geomagnetic referencing models capable of dead reckoning at GPS denied intervals. A geomagnetic matching algorithm combined with a nearest contour point of the magnetic surface is studied. Combined with an Extended Kalman filter as Inertial Navigation scheme, numerical simulations and experiments using on a quadrotor system are performed to assess the capabilities of the proposed approach at different navigation scenarios. A performance comparison between all the estimation methods is presented with the results section, and an overview of the influence of the vehicle in the measurements is presented along with the measurements gathered from experimental flights

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%

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

Event-based cameras are a new type of visual sensor that operate under a unique paradigm. These cameras provide asynchronous data on the log-level changes in light intensity for individual pixels, independent of other pixels\u27 measurements. Through the hardware-level approach to change detection, these cameras can achieve microsecond fidelity, millisecond latency, ultra-wide dynamic range, and all with very low power requirements. The advantages provided by event-based cameras make them excellent candidates for visual odometry (VO) for unmanned aerial vehicle (UAV) navigation. This document presents the research and implementation of an event-based visual inertial odometry (EVIO) pipeline, which estimates a vehicle\u27s 6-degrees-of-freedom (DOF) motion and pose utilizing an affixed event-based camera with an integrated Micro-Electro-Mechanical Systems (MEMS) inertial measurement unit (IMU). The front-end of the EVIO pipeline uses the current motion estimate of the pipeline to generate motion-compensated frames from the asynchronous event camera data. These frames are fed the back-end of the pipeline, which uses a Multi-State Constrained Kalman Filter (MSCKF) [1] implemented with Scorpion, a Bayesian state estimation framework developed by the Autonomy and Navigation Technology (ANT) Center at Air Force Institute of Technology (AFIT) [2]. This EVIO pipeline was tested on selections from the benchmark Event Camera Dataset [3]; and on a dataset collected, as part of this research, during the ANT Center\u27s first flight test with an event-based camera