In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term estimation task visual–inertial odometry (VIO), analogy to well-known visual-odometry problem. present a detailed study extended Kalman filter (EKF)-based VIO algorithms, by comparing both their theoretical properties empirical performance. show that an EKF formulation where state vector comprises sliding window poses (the multi-state-constraint (MSCKF)) attains better...

A tri-polarized 12-antenna array working in the 3.5-GHz band (3.4-3.6 GHz) for future 5G (the fifth generation mobile communication) multiple-input multiple-output (MIMO) operations smartphone is presented. In order to reduce mutual couplings and simplify design process, orthogonal polarization technique utilized. By combining a quarter mode substrate integrated wave-guide antenna two open-end slots, compact 3-antenna tri-polarization block operating achieved within small volume of 17 × 6 mm...

In this paper, we perform a rigorous analysis of EKF-based visual-inertial odometry (VIO) and present method for improving its performance. Specifically, examine the properties VIO, show that standard way computing Jacobians in filter inevitably causes inconsistency loss accuracy. This result is derived based on an observability EKF's linearized system model, which proves yaw erroneously appears to be observable. order address problem, propose modifications multi-state constraint Kalman...

An eight‐port antenna array designed for future 5G 2.6 GHz band (2550–2650 MHz) multi‐input multi‐output (MIMO) in the smartphone applications is presented. In order to enhance port isolation and reduce correlation between antennas, square loop radiating strip with orthogonal polarisation employed. The proposed composed of four pairs uniform elements that are symmetrically placed at corners main board, each pair includes a communal two independently coupled feeding strips. By exciting from...

When fusing visual and inertial measurements for motion estimation, each measurement’s sampling time must be precisely known. This requires knowledge of the offset that inevitably exists between two sensors’ data streams. The first contribution this work is an online approach estimating offset, by treating it as additional state variable to estimated along with all other variables interest (inertial measurement unit (IMU) pose velocity, biases, camera-to-IMU transformation, feature...

All existing methods for vision-aided inertial navigation assume a camera with global shutter, in which all the pixels an image are captured simultaneously. However, vast majority of consumer-grade cameras use rolling-shutter sensors, capture each row at slightly different time instant. The effects rolling shutter distortion when is motion can be very significant, and not modelled by visual-inertial motion-tracking methods. In this paper we describe first, to best our knowledge, method using...

In this paper, we propose a high-precision pose estimation algorithm for systems equipped with low-cost inertial sensors and rolling-shutter cameras. The key characteristic of the proposed method is that it performs online self-calibration camera IMU, using detailed models both their relative configuration. Specifically, estimated parameters include intrinsics (focal length, principal point, lens distortion), readout time sensor, IMU's biases, scale factors, axis misalignment, g-sensitivity,...

When measurements from multiple sensors are combined for real-time motion estimation, the time instant at which each measurement was recorded must be precisely known. In practice, however, timestamps of sensor's typically affected by a delay, is different sensor. This gives rise to temporal misalignment (i.e., offset) between sensors' data streams. this work, we propose an online approach estimating offset obtained sensors. Specifically, focus on problem estimation using visual and inertial...

A multiple-input-multiple-output (MIMO) antenna array with eight printed coplanar waveguide (CPW)-fed monopole antennas operating at 3.5 GHz (3.4-3.6 GHz) is presented.Each an Inverted-L (IL) surrounded by a parasitic IL-shorted stripe and attains compact configuration.Both the IL-monopole contribute their fundamental resonant modes to operate in desired frequency band.The neutralization line (NL) ground middle slot are used for decoupling elements array.The measurement results prototype...

External effects such as shocks and temperature variations affect the calibration of visual-inertial sensor systems thus they cannot fully rely on factory calibrations. Re-calibrations performed short user-collected datasets might yield poor performance since observability certain parameters is highly dependent motion. In addition, resource-constrained (e.g., mobile phones), full-batch approaches over longer sessions quickly become prohibitively expensive. this paper, we approach...

This paper proposes a novel inertial-aided localization approach by fusing information from multiple inertial measurement units (IMUs) and exteroceptive sensors. IMU is low-cost motion sensor which provides measurements on angular velocity gravity compensated linear acceleration of moving platform, widely used in modern systems. To date, most existing methods exploit only one single IMU. While the single-IMU yields acceptable accuracy robustness for different use cases, overall performance...

In this article, we focus on pose estimation dedicated to nonholonomic ground robots with low-cost sensors, by probabilistically fusing measurements from wheel odometers and an exteroceptive sensor. For robots, are widely used in tasks, especially applications planar scenes. However, since odometer only provides two-dimensional (2D) motion measurements, it is extremely challenging use that for accurate full 6-D (3-D position 3-D orientation) estimation. Traditional methods either approximate...

The Few-shot learning (FSL) approach distills meaningful features from a constrained sample set, allowing models to swiftly adjust novel tasks and decreasing the dependency on extensive datasets. This leverages methods involving meta-learning, transfer learning, data augmentation boost model's ability recognize new categories. In many areas of artificial intelligence, obtaining large annotated datasets is often high in financial demand extensively time-consuming, particularly specialized...

In this work, we propose a novel method for performing inertial aided navigation, by using deep neural net-works (DNNs). To date, most DNN navigation methods focus on the task of odometry, taking gyroscope and accelerometer readings as input regressing integrated IMU poses (i.e., position orientation). While design has been successfully applied number applications, it is not theoretical performance guarantee unless patterned motion involved. This inevitably leads to significantly reduced...

This paper focuses on the problem of real-time pose tracking using visual and inertial sensors in systems with limited processing power.Our main contribution is a novel approach to design estimators for these systems, which optimally utilizes available resources.Specifically, we hybrid estimator that integrates two algorithms complementary computational characteristics, namely sliding-window EKF EKF-SLAM.To decide algorithm best suited process each features at runtime, learn distribution...

In this paper, we focus on the problem of pose estimation using measurements from an inertial measurement unit and a rolling-shutter (RS) camera. The challenges posed by RS image capture are typically addressed approximate, low-dimensional representations camera motion. However, when motion contains significant accelerations (common in small-scale systems) these can lead to loss accuracy. By contrast, here describe different approach, which exploits avoid any assumptions nature trajectory....

In this paper we present a resource-adaptive framework for real-time vision-aided inertial navigation. Specifically, focus on the problem of visual-inertial odometry (VIO), in which objective is to track motion mobile platform an unknown environment. Our primary interest navigation using miniature devices with limited computational resources, similar example phone. proposed estimation consists two main components: (i) hybrid EKF estimator that integrates algorithms complementary...

In this paper, we focus on the problem of vision-based localization for ground robotic applications. recent years, camera only or camera-IMU (inertial measurement unit) based methods are widely studied, in terms theoretical properties, algorithm design, and real-world However, experimentally find that none existing is able to perform high-precision robust robots large-scale complicated 3D environments. To end, propose a novel dedicatedly designed robots, by fusing measurements from camera,...

In this paper we present a novel direct visual-inertial odometry algorithm, for estimating motion in unknown environments. The algorithm utilizes image patches extracted around features, and formulates measurement residuals the intensity space directly. One key characteristic of proposed method is that it models true irradiance at each pixel as random variable to be estimated marginalized out. formulation photometric residual explicitly accounts camera response function lens vignetting...

We present a novel method for visual mapping and localization autonomous vehicles, by extracting, modeling, optimizing semantic road elements. Specifically, our integrates cascaded deep models to detect standardized elements instead of traditional point features, seek improved pose accuracy map representation compactness. To utilize the structural we model lights signs their representative keypoints skeleton boundary, parameterize lanes via piecewise cubic splines. Based on build complete...