Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor

Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level. Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques. Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate. Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation

Micromachined vibratory gyroscopes controlled by a high order band-pass sigma delta modulator.

Abstract—This work reports on the design of novel closed-loop control systems for the sense mode of a vibratory-rate gyroscope based on a high-order sigma-delta modulator (SDM). A low-pass and two distinctive bandpass topologies are derived, and their advantages discussed. So far, most closed-loop force-feedback control systems for these sensors were based on low-pass SDM’s. Usually, the sensing element of a vibratory gyroscope is designed with a high quality factor to increase the sensitivity and, hence, can be treated as a mechanical resonator. Furthermore, the output characteristic of vibratory rate gyroscopes is narrowband amplitude- modulated signal. Therefore, a bandpass M is a more appropriate control strategy for a vibratory gyroscope than a low-pass SDM. Using a high-order bandpass SDM, the control system can adopt a much lower sampling frequency compared with a low-pass SDM while achieving a similar noise floor for a given oversampling ratio (OSR). In addition, a control system based on a high-order bandpass SDM is superior as it not only greatly shapes the quantization noise, but also alleviates tonal behavior, as is often seen in low-order SDM control systems, and has good immunities to fabrication tolerances and parameter mismatch. These properties are investigated in this study at system level

High-accuracy Motion Estimation for MEMS Devices with Capacitive Sensors

With the development of micro-electro-mechanical system (MEMS) technologies, emerging MEMS applications such as in-situ MEMS IMU calibration, medical imaging via endomicroscopy, and feedback control for nano-positioning and laser scanning impose needs for especially accurate measurements of motion using on-chip sensors. Due to their advantages of simple fabrication and integration within system level architectures, capacitive sensors are a primary choice for motion tracking in those applications. However, challenges arise as often the capacitive sensing scheme in those applications is unconventional due to the nature of the application and/or the design and fabrication restrictions imposed, and MEMS sensors are traditionally susceptible to accuracy errors, as from nonlinear sensor behavior, gain and bias drift, feedthrough disturbances, etc. Those challenges prevent traditional sensing and estimation techniques from fulfilling the accuracy requirements of the candidate applications. The goal of this dissertation is to provide a framework for such MEMS devices to achieve high-accuracy motion estimation, and specifically to focus on innovative sensing and estimation techniques that leverage unconventional capacitive sensing schemes to improve estimation accuracy. Several research studies with this specific aim have been conducted, and the methodologies, results and findings are presented in the context of three applications. The general procedure of the study includes proposing and devising the capacitive sensing scheme, deriving a sensor model based on first principles of capacitor configuration and sensing circuit, analyzing the sensor’s characteristics in simulation with tuning of key parameters, conducting experimental investigations by constructing testbeds and identifying actuation and sensing models, formulating estimation schemes is to include identified actuation dynamics and sensor models, and validating the estimation schemes and evaluating their performance against ground truth measurements. The studies show that the proposed techniques are valid and effective, as the estimation schemes adopted either fulfill the requirements imposed or improve the overall estimation performance. Highlighted results presented in this dissertation include a scale factor calibration accuracy of 286 ppm for a MEMS gyroscope (Chapter 3), an improvement of 15.1% of angular displacement estimation accuracy by adopting a threshold sensing technique for a scanning micro-mirror (Chapter 4), and a phase shift prediction error of 0.39 degree for a electrostatic micro-scanner using shared electrodes for actuation and sensing (Chapter 5).PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147568/1/davidsky_1.pd

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Characterization of MEMS Coriolis Vibratory Gyroscopes

A MEMS Gyroscope is a micromachined inertial sensor that can measure the angle of orientation or the angular rate of rotation. These devices have the potential to be used in high precision navigation, safety and consumer electronics applications. Due to their complexity, MEMS Gyroscopes are prone to have imperfections that inhibit their full potential. By deeply characterizing these sensors, it is possible to validate fabrication methodologies, apply control circuit mechanisms, and design alternative mechanical structures that improve the performance. In this project, a streamlined methodology for testing and characterizing these devices is presented and executed. Analysis to the obtained results is given. Aditionally, a prototype circuit was designed to operate the sensors in a closed-loop mode. Two families of gyroscopes with different thickness were characterized - 40 m and 100 m. The devices presented low sensitivity thresholds due to the presence of a large quadrature error. A phase sensitive demodulation solution was provided to eliminate this noise source. The 40 m presented an overall better performance. A Python Script to extract key noise performance parameters was also displayed.Giroscópios MEMS são micro sensores inerciais que conseguem medir o ângulo de orientação ou a variação ângular de uma rotação. Estes dispositivos têm o potencial de ser usados em aplicações de alta precisão para sistemas de navegação, segurança e para eletrónica comercial. Devido à sua complexidade, os Giroscópios MEMS são propensos a imperfeições que inibem o seu potencial máximo. Através da caracterização extensa destes sensores, é possível validar as metodologias de fabricação, aplicar circuitos de controlo e projetar estruturas mecânicas alternativas que melhorem a sua performance. Neste projeto é apresentada uma metodologia substanciada para testar e caracterizar estes dispositivos. Os resultados obtidos foram analisados. Adicionalmente, foi desenhado um protótipo de um circuito que opera os sensores em circuito fechado. Duas famílias de giroscópios com diferentes espessuras foram caracterizadas - 40 m e 100 m. Os dispositivos apresentaram baixos graus de sensibilidade devido a uma forte influência do erro de quadratura. Foi aplicada uma demodulação sensível à fase para melhoramento da performance. Um programa em Python para extrair parâmetros de ruído na resposta é apresentado

Development and implementation of a deflection amplification mechanism for capacitive accelerometers

Micro-Electro-Mechanical-Systems (MEMS) and especially physical sensors are part of a flourishing market ranging from consumer electronics to space applications. They have seen a great evolution throughout the last decades, and there is still considerable research effort for further improving their performance. This is reflected by the plethora of commercial applications using them but also by the demand from industry for better specifications. This demand together with the needs of novel applications fuels the research for better physical sensors.Applications such as inertial, seismic, and precision tilt sensing demand very high sensitivity and low noise. Bulk micromachined capacitive inertial sensors seem to be the most viable solution as they offer a large inertial mass, high sensitivity, good noise performance, they are easy to interface with, and of low cost. The aim of this thesis is to improve the performance of bulk micromachined capacitive sensors by enhancing their sensitivity and noise floor.MEMS physical sensors, most commonly, rely on force coupling and a resulting deflection of a proof mass or membrane to produce an output proportional to a stimulus of the physical quantity to be measured. Therefore, the sensitivity to a physical quantity may be improved by increasing the resulting deflection of a sensor. The work presented in this thesis introduces an approach based on a mechanical motion amplifier with the potential to improve the performance of mechanical MEMS sensors that rely on deflection to produce an output signal.The mechanical amplifier is integrated with the suspension system of a sensor. It comprises a system of micromachined levers (microlevers) to enhance the deflection of a proof mass caused by an inertial force. The mechanism can be used in capacitive accelerometers and gyroscopes to improve their performance by increasing their output signal. As the noise contribution of the electronic read-out circuit of a MEMS sensor is, to first order, independent of the amplitude of its input signal, the overall signal-to-noise ratio (SNR) of the sensor is improved.There is a rather limited number of reports in the literature for mechanical amplification in MEMS devices, especially when applied to amplify the deflection of inertial sensors. In this study, after a literature review, mathematical and computational methods to analyse the behaviour of microlevers were considered. By using these methods the mechanical and geometrical characteristics of microlevers components were evaluated. In order to prove the concept, a system of microlevers was implemented as a mechanical amplifier in capacitive accelerometers.All the mechanical structures were simulated using Finite Element Analysis (FEA) and system level simulations. This led to first order optimised devices that were used to design appropriate masks for fabrication. Two main fabrication processes were used; a Silicon on Insulator (SOI) process and a Silicon on Glass (SoG) process. The SOI process carried out at the University of Southampton evolved from a one mask to a two mask dicing free process with a yield of over 95%, in its third generation. The SoG is a well-established process at the University of Peking that uses three masks.The sensors were evaluated using both optical and electrical means. The results from the first prototype sensor design (1HAN) revealed an amplification factor of 40 and a mechanically amplified sensitivity of 2.39V/g. The measured natural frequency of the first mode of the sensor was at 734Hz and the full-scale measurement range was up to 7g with a maximum nonlinearity of 2%. The measurements for all the prototype sensor designs were very close to the predicted values with the highest discrepancy being 22%. The results of this research show that mechanical amplification is a very promising concept that can offer increased sensitivity in inertial sensors without increasing the noise. Experimental results show that there is plenty of room for improvement and that viable solutions may be produced by using the presented approach. The applications of this scheme are not restricted only to inertial sensors but as the results show it can be used in a broader range of micromachined devices

Constructing a reference standard for sports science and clinical movement sets using IMU-based motion capture technology

Motion analysis has improved greatly over the years through the development of low-cost inertia sensors. Such sensors have shown promising accuracy for both sport and medical applications, facilitating the possibility of a new reference standard to be constructed. Current gold standards within motion capture, such as high-speed camera-based systems and image processing, are not suitable for many movement-sets within both sports science and clinical movement analysis due to restrictions introduced by the movement sets. These restrictions include cost, portability, local environment constraints (such as light level) and poor line of sight accessibility. This thesis focusses on developing a magnetometer-less IMU-based motion capturing system to detect and classify two challenging movement sets: Basic stances during a Shaolin Kung Fu dynamic form, and severity levels from the modified UPDRS (Unified Parkinson’s Disease Rating Scale) analysis tapping exercise. This project has contributed three datasets. The Shaolin Kung Fu dataset is comprised of 5 dynamic movements repeated over 350 times by 8 experienced practitioners. The dataset was labelled by a professional Shaolin Kung Fu master. Two modified UPDRS datasets were constructed, one for each of the two locations measured. The modified UPDRS datasets comprised of 5 severity levels each with 100 self-emulated movement samples. The modified UPDRS dataset was labelled by a researcher in neuropsychological assessment. The errors associated with IMU systems has been reduced significantly through a combination of a Complementary filter and applying the constraints imposed by the range of movements available in human joints. Novel features have been extracted from each dataset. A piecewise feature set based on a moving window approach has been applied to the Shaolin Kung Fu dataset. While a combination of standard statistical features and a Durbin Watson analysis has been extracted from the modified UPDRS measurements. The project has also contributed a comparison of 24 models has been done on all 3 datasets and the optimal model for each dataset has been determined. The resulting models were commensurate with current gold standards. The Shaolin Kung Fu dataset was classified with the computational costly fine decision tree algorithm using 400 splits, resulting in: an accuracy of 98.9%, a precision of 96.9%, a recall value of 99.1%, and a F1-score of 98.0%. A novel approach of using sequential forward feature analysis was used to determine the minimum number of IMU devices required as well as the optimal number of IMU devices. The modified UPDRS datasets were then classified using a support vector machine algorithm requiring various kernels to achieve their highest accuracies. The measurements were repeated with a sensor located on the wrist and finger, with the wrist requiring a linear kernel and the finger a quadratic kernel. Both locations achieved an accuracy, precision, recall, and F1-score of 99.2%. Additionally, the project contributed an evaluation to the effect sensor location has on the proposed models. It was concluded that the IMU-based system has the potential to construct a reference standard both in sports science and clinical movement analysis. Data protection security and communication speeds were limitations in the system constructed due to the measured data being transferred from the devices via Bluetooth Low Energy communication. These limitations were considered and evaluated in the future works of this project

MEMS Gyroscopes for Consumers and Industrial Applications

none2mixedAntonello, Riccardo; Oboe, RobertoAntonello, Riccardo; Oboe, Robert

CMOS systems and circuits for sub-degree per hour MEMS gyroscopes

The objective of our research is to develop system architectures and CMOS circuits that interface with high-Q silicon microgyroscopes to implement navigation-grade angular rate sensors. The MEMS sensor used in this work is an in-plane bulk-micromachined mode-matched tuning fork gyroscope (M² – TFG ), fabricated on silicon-on-insulator substrate. The use of CMOS transimpedance amplifiers (TIA) as front-ends in high-Q MEMS resonant sensors is explored. A T-network TIA is proposed as the front-end for resonant capacitive detection. The T-TIA provides on-chip transimpedance gains of 25MΩ, has a measured capacitive resolution of 0.02aF /√Hz at 15kHz, a dynamic range of 104dB in a bandwidth of 10Hz and consumes 400μW of power. A second contribution is the development of an automated scheme to adaptively bias the mechanical structure, such that the sensor is operated in the mode-matched condition. Mode-matching leverages the inherently high quality factors of the microgyroscope, resulting in significant improvement in the Brownian noise floor, electronic noise, sensitivity and bias drift of the microsensor. We developed a novel architecture that utilizes the often ignored residual quadrature error in a gyroscope to achieve and maintain perfect mode-matching (i.e.0Hz split between the drive and sense mode frequencies), as well as electronically control the sensor bandwidth. A CMOS implementation is developed that allows mode-matching of the drive and sense frequencies of a gyroscope at a fraction of the time taken by current state of-the-art techniques. Further, this mode-matching technique allows for maintaining a controlled separation between the drive and sense resonant frequencies, providing a means of increasing sensor bandwidth and dynamic range. The mode-matching CMOS IC, implemented in a 0.5μm 2P3M process, and control algorithm have been interfaced with a 60μm thick M2−TFG to implement an angular rate sensor with bias drift as low as 0.1°/hr ℃ the lowest recorded to date for a silicon MEMS gyro.Ph.D.Committee Chair: Farrokh Ayazi; Committee Member: Jennifer Michaels; Committee Member: Levent Degertekin; Committee Member: Paul Hasler; Committee Member: W. Marshall Leac