1,624 research outputs found
A Flexible-Frame-Rate Vision-Aided Inertial Object Tracking System for Mobile Devices
Real-time object pose estimation and tracking is challenging but essential
for emerging augmented reality (AR) applications. In general, state-of-the-art
methods address this problem using deep neural networks which indeed yield
satisfactory results. Nevertheless, the high computational cost of these
methods makes them unsuitable for mobile devices where real-world applications
usually take place. In addition, head-mounted displays such as AR glasses
require at least 90~FPS to avoid motion sickness, which further complicates the
problem. We propose a flexible-frame-rate object pose estimation and tracking
system for mobile devices. It is a monocular visual-inertial-based system with
a client-server architecture. Inertial measurement unit (IMU) pose propagation
is performed on the client side for high speed tracking, and RGB image-based 3D
pose estimation is performed on the server side to obtain accurate poses, after
which the pose is sent to the client side for visual-inertial fusion, where we
propose a bias self-correction mechanism to reduce drift. We also propose a
pose inspection algorithm to detect tracking failures and incorrect pose
estimation. Connected by high-speed networking, our system supports flexible
frame rates up to 120 FPS and guarantees high precision and real-time tracking
on low-end devices. Both simulations and real world experiments show that our
method achieves accurate and robust object tracking
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
- …