Robust convex optimisation techniques for autonomous vehicle vision-based navigation

This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods

Design and Characterization of a Dust Injector for Future Studies of Tungsten Dust in the STOR-M Plasma

Dust generation from Plasma Facing Components (PFC) is a problem for tokamaks as they approach suitable reactor conditions. Tungsten dust is especially detrimental in the core, due to associated high Z bremsstrahlung power losses. As Tungsten is a primary candidate for PFC materials in large projects such as ITER, this remains a pressing issue. In order to better understand dust dynamics in tokamaks, a dust injection experiment is proposed for the Saskatchewan Torus-Modified (STOR-M). This experiment will utilize calibrated, spherical tungsten micro-particles. A known mass of these tungsten micro-particles are to be injected into STOR-M with control over the position of the dust plume. This will enable future observation and study of dust dynamics within STOR-M. In preparation for this experiment, a new dust injector has been designed, based on the fast gas valve for the University of Saskatchewan Compact Torus Injector. An experimental test apparatus was developed to characterize the dust injector. In the experiment, nitrogen gas and dust particles are injected into the test vacuum chamber under various dust injector parameters. Vacuum chamber pressures range from 10−4 - 10−5 Torr, which is within the operation range of STOR-M. These particles are then imaged with a high-speed camera via laser light scattering. Collected 12-bit raw image data was then processed and analysed. This analysis fully characterizes the dust injector in terms of the time evolution of the injector dust plume, amount of gas injected and injected dust mass

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Understanding a large-scale IPTV network via system logs

Recently, there has been a global trend among the telecommunication industry on the rapid deployment of IPTV (Internet Protocol Television) infrastructure and services. While the industry rushes into the IPTV era, the comprehensive understanding of the status and dynamics of IPTV network lags behind. Filling this gap requires in-depth analysis of large amounts of measurement data across the IPTV network. One type of the data of particular interest is device or system log, which has not been systematically studied before. In this dissertation, we will explore the possibility of utilizing system logs to serve a wide range of IPTV network management purposes including health monitoring, troubleshooting and performance evaluation, etc. In particular, we develop a tool to convert raw router syslogs to meaningful network events. In addition, by analyzing set-top box (STB) logs, we propose a series of models to capture both channel popularity and dynamics, and users' activity on the IPTV network.Ph.D.Committee Chair: Jun Xu; Committee Member: Jia Wang; Committee Member: Mostafa H. Ammar; Committee Member: Nick Feamster; Committee Member: Xiaoli M

Jointly Tracking and Separating Speech Sources Using Multiple Features and the generalized labeled multi-Bernoulli Framework

This paper proposes a novel joint multi-speaker tracking-and-separation method based on the generalized labeled multi-Bernoulli (GLMB) multi-target tracking filter, using sound mixtures recorded by microphones. Standard multi-speaker tracking algorithms usually only track speaker locations, and ambiguity occurs when speakers are spatially close. The proposed multi-feature GLMB tracking filter treats the set of vectors of associated speaker features (location, pitch and sound) as the multi-target multi-feature observation, characterizes transitioning features with corresponding transition models and overall likelihood function, thus jointly tracks and separates each multi-feature speaker, and addresses the spatial ambiguity problem. Numerical evaluation verifies that the proposed method can correctly track locations of multiple speakers and meanwhile separate speech signals

Accurate gamma and MeV-electron track reconstruction with an ultra-low diffusion Xenon/TMA TPC at 10 atm

We report the performance of a 10 atm Xenon/trimethylamine time projection chamber (TPC) for the detection of X-rays (30 keV) and gamma-rays (0.511-1.275 MeV) in conjunction with the accurate tracking of the associated electrons. When operated at such a high pressure and in similar to 1%-admixtures, trimethylamine (TMA) endows Xenon with an extremely low electron diffusion (1.3 +/- 0.13 mm-sigma (longitudinal), 0.95 +/- 0.20 mm-sigma (transverse) along 1 m drift) besides forming a convenient Penning-Fluorescent' mixture. The TPC, that houses 1.1 kg of gas in its fiducial volume, operated continuously for 100 live-days in charge amplification mode. The readout was performed through the recently introduced microbulk Micromegas technology and the AFTER chip, providing a 3D voxelization of 8 mm x 8 mm x 1.2 mm for approximately 10 cm/MeV-long electron tracks. Resolution in energy (epsilon) at full width half maximum (R) inside the fiducial volume ranged from R = 14.6% (30 keV) to R = 4.6% (1.275 MeV). This work was developed as part of the R&D program of the NEXT collaboration for future detector upgrades in the search of the neutrino-less double beta decay (beta beta 0 nu) in Xe-136, specifically those based on novel gas mixtures. Therefore we ultimately focus on the calorimetric and topological properties of the reconstructed MeV-electron tracks. In particular, the obtained energy resolution has been decomposed in its various contributions and improvements towards achieving the R =1.4%root MeV/epsilon levels obtained in small sensors are discussedThe NEXT collaboration acknowledges funding support from the following agencies and institutions: European Research Council under Advanced Grant 339787-NEXT and Starting Grant 240054-TREX, Spanish Ministerio de Economia y Competitividad under grants Consolider-Ingenio 2010 CSD2008-0037 (CUP) and CSD2007-00042 (CPAN), contracts FPA2008-03456 and FPA2009-13697; Portuguese Fundacao para a Ciencia e a Tecnologia; European FEDER under grant PPTDC/FIS/103860/2008; US Department Of Energy under contract DE-AC02-05CH11231.Gonzalez Diaz, D.; Álvarez Puerta, V.; Borges, FIG.; Camargo, M.; Carcel, S.; Cebrian, S.; Cervera, A.... (2015). Accurate gamma and MeV-electron track reconstruction with an ultra-low diffusion Xenon/TMA TPC at 10 atm. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 804:8-24. https://doi.org/10.1016/j.nima.2015.08.033S82480