Detta arbete undersöker synkroniseringstekniken dead reckoning i racingspel. Mer specifikt hur en variant, där objekts avstånd från varandra dynamiskt kontrollerar hur ofta de synkroniseras över nätverket, presterar gällande bandbreddsåtgång och konsistens jämfört med vanlig dead reckoning som alltid synkroniserar lika ofta. Det undersöks också hur banornas karaktär och egenskaper påverkar resultatet. Det visar sig att denna dynamiska dead reckoning har god potential att ge förbättringar gällande bandbreddsåtgång och att banornas utformning har stor inverkan över hur stor denna potential är.

I industriella miljöer är det avgörande att säkerställa människors säkerhet runt robotarmar för att förhindra allvarliga skador vid olyckor. Denna studie syftar till att utveckla ett realtidssystem för fara-detektering som använder datorseende och prediktiva modeller för att förbättra säkerheten. Genom att kombinera YOLOv8-algoritmen för objektigenkänning med Kalmanfiltrering (KF) och Dead Reckoning (DR) kan systemet upptäcka människors närvaro och förutsäga rörelser för att minska risken för olyckor. Det första experimentet visar att KF presterar bättre än DR, särskilt vid linjära rörelser, med lägre medelabsolutfel (MAE) och medelkvadratfel (MSE). Det andra experimentet visar att integrationen av KF med YOLOv8 resulterar i högre precision, noggrannhet och balanserad noggrannhet, även om återkallning fortfarande behöver förbättras. Dessa resultat indikerar att kombinationen av datorseende och prediktiva modeller har betydande potential att förbättra människors säkerhet. Ytterligare forskning och tester i olika scenarier är dock nödvändiga innan implementering i verkliga miljöer. / In industrial environments, ensuring human safety around robotic arms is crucial to prevent severe injuries from accidents. This study aims to develop a real-time hazard detection system using computer vision and predictive modeling techniques to improve safety. By combining the YOLOv8 object detection algorithm with Kalman Filtering (KF) and Dead Reckoning (DR), the system can detect human presence and predict movements to reduce the risk of accidents. The first experiment shows that KF outperforms DR, especially in linear movements, with lower Mean Absolute Error (MAE) and Mean Squared Error (MSE). The second experiment demonstrates that integrating KF with YOLOv8 results in higher precision, accuracy, and balanced accuracy, although recall still needs improvement. These findings indicate that combining computer vision with predictive modeling has significant potential to enhance human safety. However, further research and testing in diverse scenarios are necessary before real-world deployment.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Global Positioning System (GPS) was analyzed in terms of its repeatable accuracy, UTM projection for 2D plane coordinate system, satellite visibility performance and the horizontal dilution of positioning (HDOP). The principle of Dead Reckoning together with body coordinate system transformation was introduced. The complementary performance of GPS and DR, and GPS/DR integration using gyroscope and accelerometer were given. Test results were demonstrated that the repeatable accuracy of GPS alone is about 10 meters in open air, and DR can provide continuous positioning output within sufficient accuracy when GPS signal is outage.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Map matching method plays an important role in vehicle location and navigation systems. It employs the information in a digital map to compensate the positioning error. This paper presents a fuzzy-logic-based probabilistic map-matching algorithm used in GPS/INS integrated navigation systems, in which the reliability degree of map matching resolution is given explicitly as the decision basis in selecting matching road segment by utilizing the fuzzy comprehensive judgement. The results of experimental simulations have shown that the system performance gained significant enhancement by introducing this algorithm.

A lane position system and enhancement techniques, for increasing the robustnessand availability of such a system, are investigated. The enhancements areperformed by using additional sensor sources like map data and GPS. The thesiscontains a description of the system, two models of the system and two implementedfilters for the system. The thesis also contains conclusions and results oftheoretical and experimental tests of the increased robustness and availability ofthe system. The system can be integrated with an existing system that investigatesdriver behavior, developed for fatigue. That system was developed in aproject named Drowsi, where among others Volvo Technology participated. / Ett filpositioneringssystem undersöks och förbättringstekniker för ökandet av robusthetoch tillgängligheten av ett sådant system genom att använda ytterligaresensorkällor som kartdata och GPS. Detta examensarbete presenterar beskrivningenav ett system, två modeller och två implementerade filter. Examensarbetetinnehåller också slutsatser och resultat av teoretiska och experimentella testersom plottar och grafer av ökad robusthet och tillgängligheten av systemet. Dettasystem kan bli integrerat med ett framtaget system som tittar på körrelaterat beteendevid trötthet. Systemet är utvecklat i ett projekt kallat Drowsi, där blandandra Volvo Technology deltog.

Indoor tracking and navigation is a fundamental need for pervasive and context-aware applications. However, no practical and reliable indoor positioning solution is available at present. The major challenge of a practical solution lies in the fact that only the existing devices and infrastructure can be utilized to achieve high positioning accuracy. This thesis presents a robust indoor positioning system with the lifelong learning ability. The typical features of the proposed solution is low-cost, accurate, robust, and scalable. This system only takes the floor plan and the existing devices, e.g. phones, pads, etc. and infrastructure such as WiFi/BLE access points for the sake of practicality. This system has four closely correlated components including, non-line-of-sight identification and mitigation (NIMIT), robust pedestrian dead reckoning (R-PDR), lightweight map matching (MapCraft), and lifelong learning. NIMIT projects the received signal strength (RSS) from WiFi/BLE to locations. The R-PDR component converts the data from inertial measurement unit (IMU) sensors ubiquitous in mobile devices and wearables to the trajectories of the user. Then MapCraft fuses trajectories estimated from the R-PDR and the coarse location information from NIMIT with the floor plan and provides accurate location estimations. The lifelong learning component then learns the various parameters used in all other three components in an unsupervised manner, which continuously improves the the positioning accuracy of the system. Extensive real world experiments in multiple sites show how the proposed system outperforms state-of-the art approaches, demonstrating excellent sub-meter positioning accuracy and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position regardless of the users, devices, attachments, and environments. We believe that such an accurate and robust approach will enable always-on background localization, enabling a new era of location-aware applications to be developed.

<p>To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. </p><p>In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. </p><p>In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.</p>

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.

To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.

Robotar är en självklar del av modern automation och produktion. Användningsområdenaär många och innefattar bland annat repetitiva arbetsuppgifter ochuppgifter som kan vara hälsofarliga för oss människor, så som t.ex. målning,punktsvetsning och materialhantering. Ett problem inom robotik är att noggrant skatta position och orientering för robotensverktyg. Detta examensarbete syftar till att ta fram metoder för dennaskattning baserad på mätningar från en Inertial Measurement Unit (IMU) sommonteras vid robotens verktyg. En IMU är en kombinationsenhet som består av flera sensorer, vanligtvis accelerometeroch gyroskop. Enheten mäter då acceleration och rotationshastighetbaserat på kroppars tröghet. Examensarbetet presenterar tre metoder för att skatta position och orienteringav robotens verktyg. En skattningsmetod endast är baserad på mätningar frånIMU:n, död räkning, samt två filter där även robotkinematiken tillsammans meduppmätta motorvinklar används, extended Kalmanfilter (EKF) och komplementärfilter(CF). Resultat för skattningsmetoderna visas för experimentell data från en högpresterandeIMU tillsammans med en industrirobot med sex frihetsgrader. / Industrial robots have a well established part within modern automation and production.The uses for robots are many and include e.g. repetitive tasks, painting, spot welding and material handling. One problem in robotics is to sufficiently well estimate the position and orientation for the end effector of the robot. This thesis aims to present estimationmethods based on data from an Inertial Measurement Unit (IMU) mounted onthe end effector of the robot. An IMU is a combination unit typically containing accelerometers and gyroscopes.The unit measures acceleration and rotational speed based on the inertia of bodies. The thesis presents three methods for position and orientation estimation. One based exclusively on IMU data, dead reckoning, and two filters based on IMUdata in combination with robot kinematics and motor angles, extended Kalmanfilter (EKF) and complementary filter (CF). Results for the estimation methods are shown based on experimental data froma high-performance IMU and a industrial robot with six degrees of freedom.