Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

A particle filter to reconstruct a free-surface flow from a depth camera

We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This data assimilation scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone

Implementation of an Extended Kalman Filter Using Inertial Sensor Data for UAVs During GPS Denied Applications

Unmanned Aerial Vehicles (UAVs) are widely used across the industry and have a strong military application for defense. As UAVs become more accessible so does the increase of their applications, now being more limited by one’s imagination as opposed to the past where micro electric components were the limiting factor. Almost all of the applications require GPS or radio guidance. For more covert and longer range missions relying solely on GPS and radio is insufficient as the Unmanned Aerial System is vulnerable to malicious encounters like GPS Jamming and GPS Spoofing. For long range mission GPS denied environments are common where loss of signal is experienced. For autonomous flight GPS is a fundamental requirement. In this work an advanced inertial navigation system is proposed along with a programmable Pixhawk flight controller and Cube Black autopilot. A Raspberry Pi serves as a companion computer running autonomous flight missions and providing data acquisition. The advancement in inertial navigation comes from the implementation of a high end Analog Devices’ IMU providing input to an Extended Kalman Filter (EKF) to reduce error associated with measurement noise. The EKF is a efficient recursive computation applying the least-squares method. UAS flight controller simulations and calibrations were conducted to ensure the expected flight capabilities were achieved. The developed software and hardware was implemented in a Quadcopter build to perform flight test. Flight test data were used to analyze the performance post flight. Later, simulated feedback of the inertial navigation based state estimates (from flight test data) is performed to ensure reliable position data during GPS denied flight. The EKF applied to perform strapdown navigation was a limited success at estimating the vehicles’ inertial states but only when tuned for the specific flight trajectory. The predicted position was successfully converted to GPS data and passed to the autopilot in a LINUX based simulations ensuring autonomous mission capability is maintainable in GPS denied environments. The results from this research can be applied with ease to any vehicle operating with a Pixhawk controller and a companion computer of the appropriate processing capability

Localization Precise in Urban Area

Nowadays, stand-alone Global Navigation Satellite System (GNSS) positioning accuracy is not sufficient for a growing number of land users. Sub-meter or even centimeter accuracy is becoming more and more crucial in many applications. Especially for navigating rovers in the urban environment, final positioning accuracy can be worse as the dramatically lack and contaminations of GNSS measurements. To achieve a more accurate positioning, the GNSS carrier phase measurements appear mandatory. These measurements have a tracking error more precise by a factor of a hundred than the usual code pseudorange measurements. However, they are also less robust and include a so-called integer ambiguity that prevents them to be used directly for positioning. While carrier phase measurements are widely used in applications located in open environments, this thesis focuses on trying to use them in a much more challenging urban environment. To do so, Real Time-Kinematic (RTK) methodology is used, which is taking advantage on the spatially correlated property of most code and carrier phase measurements errors. Besides, the thesis also tries to take advantage of a dual GNSS constellation, GPS and GLONASS, to strengthen the position solution and the reliable use of carrier phase measurements. Finally, to make up the disadvantages of GNSS in urban areas, a low-cost MEMS is also integrated to the final solution. Regarding the use of carrier phase measurements, a modified version of Partial Integer Ambiguity Resolution (Partial-IAR) is proposed to convert as reliably as possible carrier phase measurements into absolute pseudoranges. Moreover, carrier phase Cycle Slip (CS) being quite frequent in urban areas, thus creating discontinuities of the measured carrier phases, a new detection and repair mechanism of CSs is proposed to continuously benefit from the high precision of carrier phases. Finally, tests based on real data collected around Toulouse are used to test the performance of the whole methodology

Indoor Geo-location And Tracking Of Mobile Autonomous Robot

The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates