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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Historically, different aspects of the configuration of an airborne instrumentation system were specified in a variety of different software applications. Instrumentation setup software handled the definition of measurements and PCM Formats while separate applications handled pre-flight checkout, calibration and post-flight data analysis. This led to the manual entry of the same data multiple times. Industry standards such as TMATS strive to address this problem by creating a data-interchange format for passing setup information from one application to another. However, a better alternative is to input all of the relevant setup information about the sensor and the measurement when it is initially created in the instrumentation vendor’s software. Furthermore, an additional performance enhancement can be achieved by adding the ability to perform sensor calibration and engineering unit conversions to pre-flight data visualization software that is tightly coupled with the instrumentation setup software. All of the setup information can then be transferred to the ground station for post-flight processing and data reduction. Detailed reports can also be generated for each measurement. This paper describes the flow of data through an integrated airborne instrumentation setup application that allows sensors and measurements to be defined, acquired, calibrated and converted from raw counts to engineering units. The process of performing a sensor calibration, configuring engineering unit conversions, and importing calibration and transducer data sheets will also be discussed.

Title: Automatic On-Line Calibration and Calibration Monitoring of Cameras and Lidars Author: Jaroslav Moravec Department: Department of Software and Computer Science Education Supervisor: doc. RNDr. Elena Šikudová, Ph.D., Department of Software and Computer Science Education Abstract: Cameras and LiDARs are important devices in the automotive indus- try as their combination provides useful information (3D coordinates of a point, its colour and intensity) for perception, localization, mapping and prediction. Successful data fusion and interpretation requires accurate calibration of intrin- sic parameters of the sensors and their 6D relative pose. In this thesis, we present a target-less calibration method on three different calibration tasks. The solu- tion is based on a robust likelihood function constructed over the reprojection error of LiDAR edges relative to image edges. When the calibration slowly wears off, our online recalibration procedure can jointly follow the extrinsic calibration drift with an average error of 0.13◦ in rotation and 4 cm in translation. Based on this recalibration tool, we are also able to monitor the calibration and detect an abrupt decalibration in a couple of seconds. And we also present a fully automatic calibration routine that estimates both the extrinsic and intrinsic...

Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to late-summer, dust transported by the Saharan Air Layer moved through the North Texas region periodically, influencing the typical monitoring pattern exhibited between low-cost sensors and reference instruments. Traditional modeling strategies were adapted to develop a new approach to calibrating low-cost particulate sensors. In this study, data collected by sensors was split according to a novel dust filter into dust and non-dust subsets prior to modeling. This approach was compared with building a single model from the data, as is typically done in other studies. Random forest and multiple linear regression algorithms were used to train models for both strategies. The best performing split-model strategy, the multiple linear regression models split according to dust and non-dust subsets (combined R2 = 0.65), outperformed the best performing single-model strategy, a random forest model (R2 = 0.49). The results from this study indicate that low-cost sensor performance can be greatly influenced by the presence of dust, and that adaptive strategies, like the ones presented in this paper, are necessary when calibrating sensors in environments that may experience pollution from inconsistent sources throughout the year.

Sensor array calibration is a major problem in engineering, to which a biological approach may provide alternative solutions. For animals, perception is relative. The aim of this thesis is to show that the relativity of perception in the animal kingdom could also be applied to robotics with promising results. This thesis explores through various behaviours and environments the properties of homeostatic mechanisms in sensory cells. It shows not only that the phenomenon can solve partial failure of sensors but also that it can be used by robots to adapt to their (changing) environment. Moreover the system shows emergent properties as well as adaptation to the robot body or its behaviour. The homeostatic mechanisms in biological neurons maintain fi ring activity between predefi ned ranges. Our model is designed to correct out of range neuron activity over a relatively long period of time (seconds or minutes). The system is implemented in a robot’s sensory neurons and is the only form of adaptability used in the central network. The robot was fi rst tested extensively with a mechanism implemented for obstacle avoidance and wall following behaviours. The robot was not only able to deal with sensor manufacture defects, but to adapt to changing environments (e.g. adapting to a narrow environment when it was originally in an open world). Emergence of non-implemented behaviours has also been observed. For example, during wall following behaviour, the robot seemed, at some point, bored. It changed the direction it was following the wall. Or we also noticed during obstacle avoidance an emerging exploratory behaviour. The model has also been tested on more complex behaviours such as skototaxis, an escape response, and phonotaxis. Again, especially with skototaxis, emergent behaviours appeared such as unpredictability on where and when the robot will be hiding. It appears that the adaptation is not only driven by the environment but by the behaviour of the robot too. It is by the complex feedback between these two things that non-implemented behaviours emerge. We showed that homeostasis can be used to improve sensory signal processing in robotics and we also found evidence that the phenomenon can be a necessary step towards better behavioural adaptation to the environment.

Video data has increased dramatically in recent years due to the prevalence of handheld cameras. Such videos, however, are usually shakier compared to videos shot by tripod-mounted cameras or cameras with mechanical stabilizers. In addition, most handheld cameras use CMOS sensors. In a CMOS sensor camera, different rows in a frame are read/reset sequentially from top to bottom. When there is fast relative motion between the scene and the video camera, a frame can be distorted because each row was captured under a different 3D-to-2D projection. This kind of distortion is known as rolling shutter effect. Digital video stabilization and rolling shutter rectification seek to remove the unwanted frame-to-frame jitter and rolling shutter effect, in order to generate visually stable and pleasant videos. In general, we need to (1) estimate the camera motion, (2) regenerate camera motion, and (3) synthesize new frames. This dissertation aims at improving the first two steps of video stabilization and rolling shutter rectification. It has been shown that the inertial sensors in handheld devices can provide more accurate and robust motion estimation compared to vision-based methods. This dissertation proposes an online camera-gyroscope calibration method for sensor fusion while a user is capturing video. The proposed method uses an implicit extended Kalman filter and is based on multiple-view geometry in a rolling shutter camera model. It is able to estimate the needed calibration parameters online with all kinds of camera motion. Given the camera motion estimated from inertial sensors after the pro- posed calibration method, this dissertation first proposes an offline motion smoothing algorithm based on a 3D rotational camera motion model. The offline motion smoothing is formulated as a geodesic-convex regression problem on the manifold of rotation matrix sequences. The formulated problem is solved by an efficient two-metric projection algorithm on the manifold. The geodesic-distance-based smoothness metric better exploits the manifold structure of sequences of rotation matrices. Then this dissertation proposes two online motion smoothing algorithms that are also based on a 3D rotational camera motion model. The first algorithm extends IIR filtering from Euclidean space to the nonlinear manifold of 3D rotation matrices. The second algorithm uses unscented Kalman filtering on a constant angular velocity model. Both offline and online motion smoothing algorithms are constrained to guarantee that no black borders intrude into the stabilized frames. / text

Cytiva i Umeå producerar medicinskteknologisk utrustning och ett av dessa system som produceras är Biacore. Dessa system används inom läkemedelsutveckling och forskning inom proteinanalys. De temperaturgivare som används i Biacore ska kalibreras noggrant då det är viktigt att det inte blir några mätfel. Målet med detta examensarbete är att utveckla nuvarande kalibreringsstationen för temperaturgivare eftersom den har blivit omodern. Fokuset ligger mest på att utveckla en ny mjukvara, dock potentiell utveckling av hårdvaran är också av intresse. Mjukvaran ska beräkna kalibreringsfaktorer för de temperaturgivare som kalibreras på ett automatiserat och effektivt sätt. Utveckling av hårdvaran kan göras genom att byta ut nuvarande referenstermometern till en ny och integrera den i kalibreringsstationen. Ett program har utvecklats med ett användargränssnitt där användaren väljer vilken typ av temperaturgivare som kalibreras och dess identifikationsnummer. Baserat på denna information hittar programmet alla relevanta filer med mätvärdena, beräknar kalibreringsfaktorer för varje temperaturgivare och sparar resultatet. Detta automatiserar beräkning och sparande av data samt effektiviserar kalibreringsprocessen. Utvecklingen av mjukvaran gjordes med kodspråket Python och biblioteket Tkinter. Hårdvaruutvecklingen har påbörjats men inte uppnåtts på grund av tidsbrist. / Cytiva in Umeå manufactures medical technological equipment. One of those systems that are produced is Biacore. Those systems are used in drug development and research in protein analysis.The temperature sensors that are used in Biacore systems must be accurately calibrated since it is of high importance to avoid errors in the measurements. The goal of this thesis is to improve current calibration station since it has gotten outdated. The focus is going to lie mostly on developing the software, although the potential development of the hardware is of interest as well. The software should be able to calculate the calibration factors for each of the temperature sensors that are calibrated in an automated and an effective way. The development of the hardware can be done by upgrading current reference thermometer to a new one and integrate it in the calibration station. A program has been developed with a user interface where the user choses what type of thetemperature sensor is to be calibrated and its identification number. Based on this information the program finds all the relevant files with the measured values, calculates calibration factors for each of the temperature sensors and saves the result. It automises calculation and saving of data as well as makes the calibrating process more effective. Development of the software was done by using Python programming language and Tkinter library. Development of the hardware has been started but was not achieved due to the lack of time.

State estimation is an essential part of any robotic autonomy solution. Continuous-time trajectory estimation is an attractive method because continuous trajectories can be queried at any time, allowing for fusion of multiple asynchronous, high-frequency measurement sources. This dissertation investigates various continuous-time estimation algorithms and their application to a handful of mobile robot autonomy and sensor calibration problems. In particular, we begin by analyzing and comparing two prominent continuous-time trajectory representations from the literature: Gaussian processes and splines, both on vector spaces and Lie groups. Our comparisons show that the two methods give comparable results so long as the same measurements and motion model are used. We then apply spline-based estimation to the problem of calibrating the extrinsic parameters between a camera and a GNSS receiver by fusing measurements from these two sensors and an IMU in continuous-time. Next, we introduce a novel estimation technique that uses the differential flatness property of dynamic systems to model the continuous-time trajectory of a robot on its flat output space, and show that estimating in the flat output space can provide superior accuracy and computation time than estimating on the configuration manifold. We use this new flatness-based estimation technique to perform pose estimation for velocity-constrained vehicles using only GNSS and IMU and show that modeling on the flat output space renders the global heading of the system observable, even when the motion of the system is insufficient to observe attitude from the measurements alone. We then show how flatness-based estimation can be used to calibrate the transformation between the dynamics coordinate frame and the coordinate frame of a sensor, along with other sensor-to-dynamics parameters, and use this calibration to improve the performance of flatness-based estimation when six-degree-of-freedom measurements are involved. Our final contribution involves nonlinear control of a quadrotor aerial vehicle. We use Lie theoretic concepts to develop a geometric attitude controller that utilizes logarithmic rotation error and prove that this controller is globally-asymptotically stable. We then demonstrate the ability of this controller to track highly-aggressive quadrotor trajectories.

The objectives of this study were to implement force sensors on the feet of a bipedal robot and analyze their response at different conditions. The data will be used to design a control strategy for the robot. The powered joints of the robot are driven by linear motors. A force sensor circuit was made and calibrated with different kinds of weight. A trajectory generator and inverse kinematics calculator for the robot were made to control the robot walking movement in an open-loop manner. The force data were taken at a certain period of time when the robot was in a standing position. Experiments with external disturbances were also performed on the robot. The ZMP position and mass of the robot were calculated by using the data of force sensors. The force sensor circuit was reliable in taking and handling the data from the sensor although the noise from the motors of the robot was present. / <p>Validerat; 20111115 (anonymous)</p>

Os robôs móveis e de serviço vêm assumindo um papel cada vez mais amplo e importante junto à sociedade moderna. Um tipo importante de robô móvel autônomo são os robôs voltados para a vigilância e segurança em ambientes internos (indoor). Estes robôs móveis de vigilância permitem a execução de tarefas repetitivas de monitoramento de ambientes, as quais podem inclusive apresentar riscos à integridade física das pessoas, podendo assim ser executadas de modo autônomo e seguro pelo robô. Este trabalho teve por objetivo o desenvolvimento dos principais módulos que compõem a arquitetura de um sistema robótico de vigilância, que incluem notadamente: (i) a aplicação de sensores com percepção 3D (Kinect) e térmica (Câmera FLIR), de relativo baixo custo, junto a este sistema robótico; (ii) a detecção de intrusos (pessoas) através do uso conjunto dos sensores 3D e térmico; (iii) a navegação de robôs móveis autônomos com detecção e desvio de obstáculos, para a execução de tarefas de monitoramento e vigilância de ambientes internos; (iv) a identificação e reconhecimento de elementos do ambiente que permitem ao robô realizar uma navegação baseada em mapas topológicos. Foram utilizados métodos de visão computacional, processamento de imagens e inteligência computacional para a realização das tarefas de vigilância. O sensor de distância Kinect foi utilizado na percepção do sistema robótico, permitindo a navegação, desvio de obstáculos, e a identificação da posição do robô em relação a um mapa topológico utilizado. Para a tarefa de detecção de pessoas no ambiente foram utilizados os sensores Kinect e câmera térmica FLIR, integrando os dados fornecidos por ambos sensores, e assim, permitindo obter uma melhor percepção do ambiente e também permitindo uma maior confiabilidade na detecção de pessoas. Como principal resultado deste trabalho foi desenvolvido um iii sistema, capaz de navegar com o uso de um mapa topológico global, capaz de se deslocar em um ambiente interno evitando colisões, e capaz de detectar a presença de seres humanos (intrusos) no ambiente. O sistema proposto foi testado em situações reais com o uso de um robô móvel Pioneer P3AT equipado com os sensores Kinect e com uma Câmera FLIR, realizando as tarefas de navegação definidas com sucesso. Outras funcionalidades foram implementadas, como o acompanhamento da pessoa (follow me) e o reconhecimento de comandos gestuais, onde a integração destes módulos com o sistema desenvolvido constituem-se de trabalhos futuros propostos / Mobile robots and service robots are increasing their applications and importance in our modern society. An important type of autonomous mobile robot application is indoor monitoring and surveillance tasks. The adoption of mobile robots for indoor surveillance tasks allows the execution of repetitive environment patrolling, which may even pose risks to the physical integrity of persons. Thus these activities can be autonomously and safely performed by security robots. This work aimed at the development of key modules and components that integrates the general architecture of a surveillance robotic system, including: (i) the development and application of a 3D perception sensor (Kinect) and a thermal sensor (FLIR camera), representing a relatively low-cost solution for mobile robot platforms; (ii) the intruder detection (people) in the environment, through the joint use of 3D and thermal sensors; (iii) the autonomous navigation of mobile robots within obstacle detection and avoidance, performing the monitoring and surveillance tasks of indoor environments; (iv) the identification and recognition of environmental features that allow the robot to perform a navigation based on topological maps. We used methods from Computer Vision, Image Processing and Computational Intelligence to carry out the implementation of the mobile robot surveillance modules. The proximity and distance measurement sensor adopted in the robotic perception system was the Kinect, allowing navigation, obstacle avoidance, and identifying key positions of the robot with respect to a topological map. For the intruder detection task we used a Kinect sensor together with a FLIR thermal camera, integrating the data obtained from both sensors, and thus allowing a better understanding of the environment, and also allowing a greater reliability in people detection. As a main result of this work, it has been v developed a system capable of navigating using a global topological map, capable of moving itself autonomously into an indoor environment avoiding collisions, and capable of detect the presence of humans (intruders) into the environment. The proposed system has been tested in real situations with the use of a Pioneer P3AT mobile robot equipped with Kinect and FLIR camera sensors, performing successfully the defined navigation tasks. Other features have also been implemented, such as following a person and recognizing gestures, proposed as future works to be integrated into the developed system