11,807 research outputs found
Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation
We present a novel method to compute the relative pose of multi-camera
systems using two affine correspondences (ACs). Existing solutions to the
multi-camera relative pose estimation are either restricted to special cases of
motion, have too high computational complexity, or require too many point
correspondences (PCs). Thus, these solvers impede an efficient or accurate
relative pose estimation when applying RANSAC as a robust estimator. This paper
shows that the 6DOF relative pose estimation problem using ACs permits a
feasible minimal solution, when exploiting the geometric constraints between
ACs and multi-camera systems using a special parameterization. We present a
problem formulation based on two ACs that encompass two common types of ACs
across two views, i.e., inter-camera and intra-camera. Moreover, the framework
for generating the minimal solvers can be extended to solve various relative
pose estimation problems, e.g., 5DOF relative pose estimation with known
rotation angle prior. Experiments on both virtual and real multi-camera systems
prove that the proposed solvers are more efficient than the state-of-the-art
algorithms, while resulting in a better relative pose accuracy. Source code is
available at https://github.com/jizhaox/relpose-mcs-depth
A Map-Free LiDAR-Based System for Autonomous Navigation in Vineyards
Agricultural robots have the potential to increase production yields and
reduce costs by performing repetitive and time-consuming tasks. However, for
robots to be effective, they must be able to navigate autonomously in fields or
orchards without human intervention. In this paper, we introduce a navigation
system that utilizes LiDAR and wheel encoder sensors for in-row, turn, and
end-row navigation in row structured agricultural environments, such as
vineyards. Our approach exploits the simple and precise geometrical structure
of plants organized in parallel rows. We tested our system in both simulated
and real environments, and the results demonstrate the effectiveness of our
approach in achieving accurate and robust navigation. Our navigation system
achieves mean displacement errors from the center line of 0.049 m and 0.372 m
for in-row navigation in the simulated and real environments, respectively. In
addition, we developed an end-row points detection that allows end-row
navigation in vineyards, a task often ignored by most works
CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model
In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers
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Landfill site trees: Potential source or sink of greenhouse gases?
Tree stems can transport greenhouse gases (GHGs) produced belowground to the atmosphere. Previous studies in natural wetland and upland ecosystems have quantified tree stem fluxes of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O). However, tree stem GHG fluxes have not previously been measured in the context of managed environments. The work presented in this thesis aimed to quantify GHG fluxes from tree stems on closed landfill sites.
To investigate the potential for trees growing on closed landfill sites to act as conduits for GHGs produced belowground to the atmosphere, GHG fluxes were measured from tree stem and soil surfaces. In situ measurements from a closed landfill site in the UK were examined for spatial and temporal patterns and evaluated against data from a comparable non-landfill area. Measurements were also conducted from landfill sites in the UK with varying management practices and different tree species present. The resulting flux values were scaled up to estimate the magnitude of tree stem GHG fluxes from closed landfills at a national level.
The findings presented here show evidence of tree mediated GHG transport on closed landfill sites and temporal variations in fluxes from tree stems were also observed, with generally higher fluxes in the summer months. Stem CH4 fluxes varied between trees growing on landfill sites with different management practices. Additionally, stem N2O fluxes displayed spatial patterns, with decreasing emissions at increased height from the forest floor, indicating an underground source. Evidence suggested that GHG fluxes from closed landfills are influenced by factors including the quantity of GHG produced in the waste (linked to the age of the site), the susceptibility of the area to waterlogging and landfill management techniques put in place upon closure (for example, clay caps, cover soils and gas extraction). Upscaled CH4 and N2O flux values from tree stems on closed landfill sites corresponded to less than 1% of the total CH4 and N2O emissions reported from UK landfills in 2020.
Overall, results indicated that measuring soil fluxes alone from forested landfill sites would result in an underestimation of the total surface fluxes. However, the emission rates from tree stems on closed landfills observed in this thesis do not exceed those in natural ecosystems. Therefore, with careful planning and management, the recommendation is that trees can be planted on closed landfill sites in the UK without emitting atypical levels of GHGs. However, including gas fluxes from tree stems on closed landfills would increase the accuracy of GHG budgets at national and global levels
Technology for Low Resolution Space Based RSO Detection and Characterisation
Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
Polarimetric airborne scientific instrument, mark 2, an ice‐sounding airborne synthetic aperture radar for subglacial 3D imagery
Polarimetric Airborne Scientific INstrument, mark 2 (PASIN2) is a 150 MHz coherent pulsed radar with the purpose of deep ice sounding for bedrock, subglacial channels and ice-water interface detection in Antarctica. It is designed and operated by the British Antarctic Survey from 2014. With multiple antennas, oriented along and across-track, for transmission and reception, it enables polarimetric 3D estimation of the ice base with a single pass, reducing the gridding density of the survey paths. The off-line data processing stream consists of channel calibration; 2D synthetic aperture radar (SAR) imaging based on back-projection, for along-track and range dimensions; and finally, a direction of arrival estimation (DoA) of the remaining across-track angle, by modifying the non-linear MUSIC algorithm. Calibration flights, during the Antarctic Summer campaigns in 16/17 and 19/20 seasons, assessed and validated the instrument and processing performances. Imaging flights over ice streams and ice shelves close to grounding lines demonstrate the 3D sensing capabilities. By resolving directional ambiguities and accounting for reflector across-track location, the true ice thickness and bed elevation are obtained, thereby removing the error of the usual assumption of vertical DoA, that greatly influence the output of flow models of ice dynamics
Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation
Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.
To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.
A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.
The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D
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