743 research outputs found
Deeply-Integrated Feature Tracking for Embedded Navigation
The Air Force Institute of Technology (AFIT) is investigating techniques to improve aircraft navigation using low-cost imaging and inertial sensors. Stationary features tracked within the image are used to improve the inertial navigation estimate. These features are tracked using a correspondence search between frames. Previous research investigated aiding these correspondence searches using inertial measurements (i.e., stochastic projection). While this research demonstrated the benefits of further sensor integration, it still relied on robust feature descriptors (e.g., SIFT or SURF) to obtain a reliable correspondence match in the presence of rotation and scale changes. Unfortunately, these robust feature extraction algorithms are computationally intensive and require significant resources for real-time operation. Simpler feature extraction algorithms are much more efficient, but their feature descriptors are not invariant to scale, rotation, or affine warping which limits matching performance during arbitrary motion. This research uses inertial measurements to predict not only the location of the feature in the next image but also the feature descriptor, resulting in robust correspondence matching with low computational overhead. This novel technique, called deeply-integrated feature tracking, is exercised using real imagery. The term deep integration is derived from the fact inertial information is used to aid the image processing. The navigation experiments presented demonstrate the performance of the new algorithm in relation to the previous work. Further experiments also investigate a monocular camera setup necessary for actual flight testing. Results show that the new algorithm is 12 times faster than its predecessor while still producing an accurate trajectory. Thirty-percent more features were initialized using the new tracker over the previous algorithm. However, low-level aiding techniques successfully reduced the number of features initialized indicating a more robust tracking solution through deep integration
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
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters
Monocular visual-inertial odometry (VIO) is a low-cost solution to provide
high-accuracy, low-drifting pose estimation. However, it has been meeting
challenges in vehicular scenarios due to limited dynamics and lack of stable
features. In this paper, we propose Ground-VIO, which utilizes ground features
and the specific camera-ground geometry to enhance monocular VIO performance in
realistic road environments. In the method, the camera-ground geometry is
modeled with vehicle-centered parameters and integrated into an
optimization-based VIO framework. These parameters could be calibrated online
and simultaneously improve the odometry accuracy by providing stable
scale-awareness. Besides, a specially designed visual front-end is developed to
stably extract and track ground features via the inverse perspective mapping
(IPM) technique. Both simulation tests and real-world experiments are conducted
to verify the effectiveness of the proposed method. The results show that our
implementation could dramatically improve monocular VIO accuracy in vehicular
scenarios, achieving comparable or even better performance than state-of-art
stereo VIO solutions. The system could also be used for the auto-calibration of
IPM which is widely used in vehicle perception. A toolkit for ground feature
processing, together with the experimental datasets, would be made open-source
(https://github.com/GREAT-WHU/gv_tools)
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud
representation of the scene that does not model the topology of the
environment. A 3D mesh instead offers a richer, yet lightweight, model.
Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks
triangulated by a VIO algorithm often results in a mesh that does not fit the
real scene. In order to regularize the mesh, previous approaches decouple state
estimation from the 3D mesh regularization step, and either limit the 3D mesh
to the current frame or let the mesh grow indefinitely. We propose instead to
tightly couple mesh regularization and state estimation by detecting and
enforcing structural regularities in a novel factor-graph formulation. We also
propose to incrementally build the mesh by restricting its extent to the
time-horizon of the VIO optimization; the resulting 3D mesh covers a larger
portion of the scene than a per-frame approach while its memory usage and
computational complexity remain bounded. We show that our approach successfully
regularizes the mesh, while improving localization accuracy, when structural
regularities are present, and remains operational in scenes without
regularities.Comment: 7 pages, 5 figures, ICRA accepte
Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory
To build commercial robots, skid-steering mechanical design is of increased
popularity due to its manufacturing simplicity and unique mechanism. However,
these also cause significant challenges on software and algorithm design,
especially for pose estimation (i.e., determining the robot's rotation and
position), which is the prerequisite of autonomous navigation. While the
general localization algorithms have been extensively studied in research
communities, there are still fundamental problems that need to be resolved for
localizing skid-steering robots that change their orientation with a skid. To
tackle this problem, we propose a probabilistic sliding-window estimator
dedicated to skid-steering robots, using measurements from a monocular camera,
the wheel encoders, and optionally an inertial measurement unit (IMU).
Specifically, we explicitly model the kinematics of skid-steering robots by
both track instantaneous centers of rotation (ICRs) and correction factors,
which are capable of compensating for the complexity of track-to-terrain
interaction, the imperfectness of mechanical design, terrain conditions and
smoothness, and so on. To prevent performance reduction in robots' lifelong
missions, the time- and location- varying kinematic parameters are estimated
online along with pose estimation states in a tightly-coupled manner. More
importantly, we conduct in-depth observability analysis for different sensors
and design configurations in this paper, which provides us with theoretical
tools in making the correct choice when building real commercial robots. In our
experiments, we validate the proposed method by both simulation tests and
real-world experiments, which demonstrate that our method outperforms competing
methods by wide margins.Comment: 18 pages in tota
Homography-Based State Estimation for Autonomous Exploration in Unknown Environments
This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position
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