<p>For many years, terrain navigation has been successfully used in military airborne applications. Terrain navigation can essentially improve the performance of traditional inertial-based navigation. The latter is typically built around gyros and accelerometers, measuring the kinetic state changes. Although inertial-based systems benefit from their high independence, they, unfortunately, suffer from increasing error-growth due to accumulation of continuous measurement errors. </p><p>Undersea, the number of options for navigation support is fairly limited. Still, the navigation accuracy demands on autonomous underwater vehicles are increasing. For many military applications, surfacing to receive a GPS position- update is not an option. Lately, some attention has, instead, shifted towards terrain aided navigation. </p><p>One fundamental aim of this work has been to show what can be done within the field of terrain aided underwater navigation, using relatively simple means. A concept has been built around a narrow-beam altimeter, measuring the depth directly beneath the vehicle as it moves ahead. To estimate the vehicle location, based on the depth measurements, a particle filter algorithm has been implemented. A number of MATLAB simulations have given a qualitative evaluation of the chosen algorithm. In order to acquire data from actual underwater terrain, a small area of the Swedish lake, Lake Vättern has been charted. Results from simulations made on this data strongly indicate that the particle filter performs surprisingly well, also within areas containing relatively modest terrain variation.</p>

<p>The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS.</p> / <p>Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare än ett som använder GPS.Det här examensarbetet presenterar två sätt att fusionera mätningar från etttröghetssystem och en kamera. Gemensamt för båda är att en hörndetektor, Harriscorner detector, används för att hitta korresponderande punkter mellan kamerabildernaoch ett ortofoto. Systemen utvärderas på simulerat data. Resultatenvisar att för simulerade data så fungerar den implementerade hörndetektorn ochatt prestanda i nivå med ett GPS-baserat system uppnås.</p>

<p>I detta examensarbete har ett system för navigering och styrning av ett autonomt fordon implementerats. Syftet med detta arbete är att vidareutveckla fordonet som ska användas vid utvärdering av banplaneringsalgoritmer och studier av andra autonomifunktioner. Med hjälp av olika sensormodeller och sensorkonfigurationer går det även att utvärdera olika strategier för navigering. Arbetet har utförts utgående från en given plattform där fordonet endast använder sig av enkla ultraljudssensorer samt pulsgivare på hjulen för att mäta förflyttningar. Fordonet kan även autonomt navigera samt följa en enklare given bana i en känd omgivning. Systemet använder ett partikelfilter för att skatta fordonets tillstånd med hjälp av modeller för fordon och sensorer.</p><p>Arbetet är en fortsättning på projektet Collision Avoidance för autonomt fordon som genomfördes vid Linköpings universitet våren 2005.</p> / <p>In this thesis a system for navigation and control of an autonomous ground vehicle has been implemented. The purpose of this thesis is to further develop the vehicle that is to be used in studies and evaluations of path planning algorithms as well as studies of other autonomy functions. With different sensor configurations and sensor models it is also possible to evaluate different strategies for navigation. The work has been performed using a given platform which measures the vehicle’s movement using only simple ultrasonic sensors and pulse encoders. The vehicle is able to navigate autonomously and follow a simple path in a known environment. The state estimation is performed using a particle filter.</p><p>The work is a continuation of a previous project, Collision Avoidance för autonomt fordon, at Linköpings University in the spring of 2005.</p>

<p>Scania CV AB is a large manufacturer of heavy duty trucks that, with an increasingly stricter emission legislation, have a rising demand for an effective On Board Diagnosis (OBD) system. One idea for improving the OBD system is to employ a model for the construction of an observer based diagnosis system. The proposal in this report is, because of a nonlinear model, to use a nonlinear filtering method for improving the needed state estimates. Two nonlinear filters are tested, the Particle Filter (PF) and the Extended Kalman Filter (EKF). The primary objective is to evaluate the use of the PF for Fault Detection and Isolation (FDI), and to compare the result against the use of the EKF.</p><p>With the information provided by the PF and the EKF, two residual based diagnosis systems and two likelihood based diagnosis systems are created. The results with the PF and the EKF are evaluated for both types of systems using real measurement data. It is shown that the four systems give approximately equal results for FDI with the exception that using the PF is more computational demanding than using the EKF. There are however some indications that the PF, due to the nonlinearities, could offer more if enough CPU time is available.</p>

Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games, cheating characters may not behave in a human-like manner and can contribute to a lack of player enjoyment if caught. This work investigates the problem of predicting opponent positions in the video game Counter-Strike: Source without cheating. Prediction models are machine-learned from records of past matches and are informed only by game information available to a human player. Results show that the best models estimate opponent positions with similar or better accuracy than human experts. Moreover, the mistakes these models make are closer to human predictions than actual opponent locations perturbed by a corresponding amount of Gaussian noise.

As global demand for nuclear energy and threats to nuclear security increase, the need for verification of the peaceful application of nuclear materials and technology also rises. In accordance with the Nuclear Nonproliferation Treaty, the International Atomic Energy Agency is tasked with verification of the declared enrichment activities of member states. Due to the increased cost of inspection and verification of a globally growing nuclear energy industry, remote process monitoring has been proposed as part of a next-generation, information-driven safeguards program. To further enhance this safeguards approach, it is proposed that process monitoring data may be used to not only verify the past but to anticipate the future via prognostic analysis. While prognostic methods exist for health monitoring of physical processes, the literature is absent of methods to predict the outcome of decision-based events, such as the production of undeclared enriched uranium. This dissertation introduces a method to predict the time at which a significant quantity of unaccounted material is expected to be diverted during an enrichment process. This method utilizes a particle filter to model the data and provide a Type III (degradation-based) prognostic estimate of time to diversion of a significant quantity. Measurement noise for the particle filter is estimated using historical data and may be updated with Bayesian estimates from the analyzed data. Dynamic noise estimates are updated based on observed changes in process data. The reliability of the prognostic model for a given range of data is validated via information complexity scores and goodness of fit statistics. The developed prognostic method is tested using data produced from the Oak Ridge Mock Feed and Withdrawal Facility, a 1:100 scale test platform for developing gas centrifuge remote monitoring techniques. Four case studies are considered: no diversion, slow diversion, fast diversion, and intermittent diversion. All intervals of diversion and non-diversion were correctly identified and significant quantity diversion time was accurately estimated. A diversion of 0.8 kg over 85 minutes was detected after 10 minutes and predicted to be 84 minutes and 10 seconds after 46 minutes and 40 seconds with an uncertainty of 2 minutes and 52 seconds.

For the environment of target tracking with highly non-linear models and non-Gaussian noise, the tracking performance of the particle filter is better than extended Kalman filter; in addition, the design of particle filter is simpler, so it is quite suitable for the realistic environment. However, particle filter depends on the probability model of the noise. If the knowledge of the noise is incorrect, the tracking performance of the particle filter will degrade severely. To tackle the problem, cost function-based particle filters have been studied. Though suffering from minor degradation on the performance, the cost function-based particle filters do not need probability assumptions of the noises. The application of cost function-based particle filters will be more robust in any realistic environment. Cost function-based particle filters will enable maneuvering multiple target tracking to be suitable for any environment because it does not depend on the noise model. The difficulty lies in the link between the estimator and data association. The likelihood function are generally obtained from the algorithm of the data association; while cost functions are used in the cost function-based particle filter for moving the particles and update the corresponding weights without probability assumptions on the noises. The thesis is focused on the combination of data association and cost function-based particle filter, in order to make the algorithm of multiple target tracking more robust in noisy environments.

Recovery of human pose from videos has become a highly active research area in the last decade because of many attractive potential applications, such as surveillance, non-intrusive motion analysis and natural human machine interaction. Video based full body pose estimation is a very challenging task, because of the high degree of articulation of the human body, the large variety of possible human motions, and the diversity of human appearances. Methods for tackling this problem can be roughly categorized as either discriminative or generative. Discriminative methods can work on single images, and are able to recover the human poses efficiently. However, the accuracy and generality largely depend on the training data. Generative approaches usually formulate the problem as a tracking problem and adopt an explicit human model. Although arbitrary motions can be tracked, such systems usually have difficulties in adapting to different subjects and in dealing with tracking failures. In this thesis, an accurate, efficient and robust human pose tracking system from a single view camera is developed, mainly following a generative approach. A novel discriminative feature is also proposed and integrated into the tracking framework to improve the tracking performance. The human pose tracking system is proposed within a particle filtering framework. A reconfigurable skeleton model is constructed based on the Acclaim Skeleton File convention. A basic particle filter is first implemented for upper body tracking, which fuses time efficient cues from monocular sequences and achieves real-time tracking for constrained motions. Next, a 3D surface model is added to the skeleton model, and a full body tracking system is developed for more general and complex motions, assuming a stereo camera input. Partitioned sampling is adopted to deal with the high dimensionality problem, and the system is capable of running in near real-time. Multiple visual cues are investigated and compared, including a newly developed explicit depth cue. Based on the comparative analysis of cues, which reveals the importance of depth and good bottom-up features, a novel algorithm for detecting and identifying endpoint body parts from depth images is proposed. Inspired by the shape context concept, this thesis proposes a novel Local Shape Context (LSC) descriptor specifically for describing the shape features of body parts in depth images. This descriptor describes the local shape of different body parts with respect to a given reference point on a human silhouette, and is shown to be effective at detecting and classifying endpoint body parts. A new type of interest point is defined based on the LSC descriptor, and a hierarchical interest point selection algorithm is designed to further conserve computational resources. The detected endpoint body parts are then classified according to learned models based on the LSC feature. The algorithm is tested using a public dataset and achieves good accuracy with a 100Hz processing speed on a standard PC. Finally, the LSC descriptor is improved to be more generalized. Both the endpoint body parts and the limbs are detected simultaneously. The generalized algorithm is integrated into the tracking framework, which provides a very strong cue and enables tracking failure recovery. The skeleton model is also simplified to further increase the system efficiency. To evaluate the system on arbitrary motions quantitatively, a new dataset is designed and collected using a synchronized Kinect sensor and a marker based motion capture system, including 22 different motions from 5 human subjects. The system is capable of tracking full body motions accurately using a simple skeleton-only model in near real-time on a laptop PC before optimization.

The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS. / Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare än ett som använder GPS.Det här examensarbetet presenterar två sätt att fusionera mätningar från etttröghetssystem och en kamera. Gemensamt för båda är att en hörndetektor, Harriscorner detector, används för att hitta korresponderande punkter mellan kamerabildernaoch ett ortofoto. Systemen utvärderas på simulerat data. Resultatenvisar att för simulerade data så fungerar den implementerade hörndetektorn ochatt prestanda i nivå med ett GPS-baserat system uppnås.

The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability. / Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.