53 research outputs found
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Visual-Inertial Odometry on Resource-Constrained Systems
No full textIn this work, we focus on the problem of pose estimation in unknown environments, using the measurements from an inertial measurement unit (IMU) and a single camera. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry (VO) problem. Our focus is on developing VIO algorithms for high-precision, consistent motion estimation in real time. The majority of VIO algorithms proposed to date have been developed for systems which are equipped with high-end processors and high-quality sensors. By contrast, we are interested in tracking the motion of systems that are small and inexpensive, and are equipped with limited processing and sensing resources.Such resource-constrained systems are common in application areas such as micro aerial vehicles, mobile phones, and augmented reality (AR) devices. Endowing such systems with the capability to accurately track their poses will create a host of new opportunities for commercial applications, and lower the barrier to entry in robotics research and development.Performing accurate motion estimation on resource-constrained systems requires novel methodologies to address the challenges caused by the limited sensing and processing capabilities, and to provide guarantees for the optimal utilization of these resources. To this end, in this work, we focus on developing novel, resource-adaptive VIO algorithms based on the extended Kalman filter (EKF) formulation. Specifically, we (i) analyze the properties and performance of existing EKF-basedVIO approaches, and propose a novel estimator design method, which ensures the correct observability properties of the linearized system models to improve the estimates' accuracy and consistency, (ii) present a methodology for minimizing the computational cost of the EKF-VIO algorithms, which relies on online optimization of the estimator's parameters based on the properties of the environment, (iii) propose an algorithm for joint online calibration of the spatial and temporal relationship between the visual and inertial sensors, and (iv) propose high-fidelity sensor models that enable us to process the measurements captured by rolling-shutter cameras and low-cost inertial sensors. We evaluate our estimators with various simulated and real-world data sets, which demonstrate that our proposed formulations are able to consistently and accurately track the pose of resource-constrained systems in real time
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Towards Semi-Dense Indirect Visual-Inertial Odometry
No full textIn this work, we focus on motion estimation in unknown environments using measurements provided by an inertial measurement unit (IMU) and a monocular camera. We are interested in estimating the trajectory of a moving platform, a problem typically termed visual-inertial odometry (VIO). Most of existing methods for vision-aided inertial localization rely on the detection and tracking of point features in the images. These approaches greatly reduce the amount of data to process in each image, and are thus suitable for application in resource-constrained systems. However these treatments inevitably discard information that is beneficial for motion estimation, since not all parts of the images are used. Therefore there has been growing interest in direct methods which rely on directly using image intensities for motion estimation. Although this approach makes it possible to use more pixel locations, it also suffers a number of shortcomings (e.g. non-Lambertian surface properties, and the dependence on camera photometric parameters). By contrast we are interested in approaches that rely on geometry of straight lines or image contours rather than raw image intensities (thus the proposed approaches are indirect methods). This enables our algorithms to operate in environments where point features are sparse, while circumventing the shortcomings of direct methods.This thesis is divided into two main parts. We first propose a visual-inertial localization algorithm that employs lines as measurements, in addition to traditional point features. Specifically, a novel parameterization and measurement model for line features are proposed, and we show how line features can be used for self-calibration of the IMU and camera. Our results demonstrate that the proposed approach not only leads to improved localization accuracy in point-feature-poor environments, but also reduces calibration errors compared to the point-only approach. We then propose a method for monocular visual-inertial odometry that utilizes image edges as measurements. We here relax the requirement for having straight lines, and do not employ any assumption on the geometry of the scene. This enables us to use measurements from all image areas with significant gradient. In addition, we have proposed a novel edge parameterization and measurement model that explicitly account for the fact that edge points can only provide useful information in the direction of the image gradient. Through both Monte-Carlo simulations, as well as results from real-world experiments, we demonstrate that the proposed edge-based approach to visual-inertial odometry is consistent, and outperforms the point-based one
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Visual-Inertial Odometry: Efficiency and Accuracy
No full textAccurate localization is essential in many applications such as robotics, unmanned aerial vehicles, virtual reality, and augmented reality. In this work, we focus on the localization of a platform in an unknown environment, with an inertial measurement unit (IMU) and a monocular camera. This task is often termed visual-inertial odometry (VIO).In this work, we focus on improving the computational efficiency and accuracy of the state of the art in VIO algorithms. Specifically, to improve computational efficiency we first propose the Decoupled Estimate-Error Parameterization (DEEP) that addresses the high dimensionality of the estimation problem. An extended Kalman filter (EKF) VIO algorithm is re-formulated in the DEEP framework, using measurements from a rolling-shutter camera. The DEEP-EKF formulation is evaluated through Monte-Carlo simulations and real-world experiments, which shows substantial computational gains, while incurring only a small loss of estimation performance.To achieve improved estimation accuracy, we describe three key methods. First, we propose high-fidelity sensor modeling, along with online self-calibration. An additional contribution of the work is the novel method for processing the measurements of the rolling-shutter camera, which employs an approximate representation of the estimation errors, instead of the state itself. Both Monte-Carlo simulations and real-world experiments are conducted to demonstrate the improved estimation precision of the proposed approach compared to existing ones.We also propose a direct VIO algorithm, which utilizes image patches extracted around image features, and formulates measurement residuals in the image intensity space directly. A detailed evaluation of the algorithm demonstrates that the use of photometric residuals results in increased pose estimation accuracy, with approximately 23% lower estimation errors, on average in our testing.At last, we extend the direct VIO formulation to a semi-dense framework, where all informative areas in images are used. Photometric triangulation and a novel noise model, which accounts for noise during the image formation and interpolation errors, are employed in this work. Through Monte-Carlo simulations and real-world experiments, we demonstrate that the proposed semi-dense VIO outperforms the direct VIO and the point-feature-based method, in terms of the estimation accuracy
An Efficient Control-driven Period Optimization Algorithm for Distributed Real-time Systems
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Η επίπτωση της ίσχαιμης περίδεσης κατά τις αρθροπλαστικές του γόνατος, στους φλεγμονώδεις αιματολογικούς δείκτες και στα αέρια αίματος
No full textAn Efficient Control-driven Period Optimization Algorithm for Distributed Real-time Systems
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