34 research outputs found
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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
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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
Vision-aided inertial navigation with rolling-shutter cameras
No full textAbstract In this paper, we focus on the problem of pose estimation using measurements from an inertial measurement unit and a rolling-shutter (RS
