1,737 research outputs found
Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles
Today, rail vehicle localization is based on infrastructure-side Balises
(beacons) together with on-board odometry to determine whether a rail segment
is occupied. Such a coarse locking leads to a sub-optimal usage of the rail
networks. New railway standards propose the use of moving blocks centered
around the rail vehicles to increase the capacity of the network. However, this
approach requires accurate and robust position and velocity estimation of all
vehicles. In this work, we investigate the applicability, challenges and
limitations of current visual and visual-inertial motion estimation frameworks
for rail applications. An evaluation against RTK-GPS ground truth is performed
on multiple datasets recorded in industrial, sub-urban, and forest
environments. Our results show that stereo visual-inertial odometry has a great
potential to provide a precise motion estimation because of its complementing
sensor modalities and shows superior performance in challenging situations
compared to other frameworks
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
In recent years, vision-aided inertial odometry for state estimation has
matured significantly. However, we still encounter challenges in terms of
improving the computational efficiency and robustness of the underlying
algorithms for applications in autonomous flight with micro aerial vehicles in
which it is difficult to use high quality sensors and pow- erful processors
because of constraints on size and weight. In this paper, we present a
filter-based stereo visual inertial odometry that uses the Multi-State
Constraint Kalman Filter (MSCKF) [1]. Previous work on stereo visual inertial
odometry has resulted in solutions that are computationally expensive. We
demonstrate that our Stereo Multi-State Constraint Kalman Filter (S-MSCKF) is
comparable to state-of-art monocular solutions in terms of computational cost,
while providing signifi- cantly greater robustness. We evaluate our S-MSCKF
algorithm and compare it with state-of-art methods including OKVIS, ROVIO, and
VINS-MONO on both the EuRoC dataset, and our own experimental datasets
demonstrating fast autonomous flight with maximum speed of 17.5m/s in indoor
and outdoor environments. Our implementation of the S-MSCKF is available at
https://github.com/KumarRobotics/msckf_vio.Comment: Submitted to RAL and ICRA 201
The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Event based cameras are a new passive sensing modality with a number of
benefits over traditional cameras, including extremely low latency,
asynchronous data acquisition, high dynamic range and very low power
consumption. There has been a lot of recent interest and development in
applying algorithms to use the events to perform a variety of 3D perception
tasks, such as feature tracking, visual odometry, and stereo depth estimation.
However, there currently lacks the wealth of labeled data that exists for
traditional cameras to be used for both testing and development. In this paper,
we present a large dataset with a synchronized stereo pair event based camera
system, carried on a handheld rig, flown by a hexacopter, driven on top of a
car and mounted on a motorcycle, in a variety of different illumination levels
and environments. From each camera, we provide the event stream, grayscale
images and IMU readings. In addition, we utilize a combination of IMU, a
rigidly mounted lidar system, indoor and outdoor motion capture and GPS to
provide accurate pose and depth images for each camera at up to 100Hz. For
comparison, we also provide synchronized grayscale images and IMU readings from
a frame based stereo camera system.Comment: 8 pages, 7 figures, 2 tables. Website:
https://daniilidis-group.github.io/mvsec/. Video:
https://www.youtube.com/watch?v=AwRMO5vFgak. Updated website and video in
comments, DO
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
A monocular visual-inertial system (VINS), consisting of a camera and a
low-cost inertial measurement unit (IMU), forms the minimum sensor suite for
metric six degrees-of-freedom (DOF) state estimation. However, the lack of
direct distance measurement poses significant challenges in terms of IMU
processing, estimator initialization, extrinsic calibration, and nonlinear
optimization. In this work, we present VINS-Mono: a robust and versatile
monocular visual-inertial state estimator.Our approach starts with a robust
procedure for estimator initialization and failure recovery. A tightly-coupled,
nonlinear optimization-based method is used to obtain high accuracy
visual-inertial odometry by fusing pre-integrated IMU measurements and feature
observations. A loop detection module, in combination with our tightly-coupled
formulation, enables relocalization with minimum computation overhead.We
additionally perform four degrees-of-freedom pose graph optimization to enforce
global consistency. We validate the performance of our system on public
datasets and real-world experiments and compare against other state-of-the-art
algorithms. We also perform onboard closed-loop autonomous flight on the MAV
platform and port the algorithm to an iOS-based demonstration. We highlight
that the proposed work is a reliable, complete, and versatile system that is
applicable for different applications that require high accuracy localization.
We open source our implementations for both PCs and iOS mobile devices.Comment: journal pape
The Open Vision Computer: An Integrated Sensing and Compute System for Mobile Robots
In this paper we describe the Open Vision Computer (OVC) which was designed
to support high speed, vision guided autonomous drone flight. In particular our
aim was to develop a system that would be suitable for relatively small-scale
flying platforms where size, weight, power consumption and computational
performance were all important considerations. This manuscript describes the
primary features of our OVC system and explains how they are used to support
fully autonomous indoor and outdoor exploration and navigation operations on
our Falcon 250 quadrotor platform.Comment: 7 pages, 13 figures, conferenc
Feature-based visual odometry prior for real-time semi-dense stereo SLAM
Robust and fast motion estimation and mapping is a key prerequisite for
autonomous operation of mobile robots. The goal of performing this task solely
on a stereo pair of video cameras is highly demanding and bears conflicting
objectives: on one hand, the motion has to be tracked fast and reliably, on the
other hand, high-level functions like navigation and obstacle avoidance depend
crucially on a complete and accurate environment representation. In this work,
we propose a two-layer approach for visual odometry and SLAM with stereo
cameras that runs in real-time and combines feature-based matching with
semi-dense direct image alignment. Our method initializes semi-dense depth
estimation, which is computationally expensive, from motion that is tracked by
a fast but robust keypoint-based method. Experiments on public benchmark and
proprietary datasets show that our approach is faster than state-of-the-art
methods without losing accuracy and yields comparable map building
capabilities. Moreover, our approach is shown to handle large inter-frame
motion and illumination changes much more robustly than its direct
counterparts
CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments
The CREATE database is composed of 14 hours of multimodal recordings from a
mobile robotic platform based on the iRobot Create. The various sensors cover
vision, audition, motors and proprioception. The dataset has been designed in
the context of a mobile robot that can learn multimodal representations of its
environment, thanks to its ability to navigate the environment. This ability
can also be used to learn the dependencies and relationships between the
different modalities of the robot (e.g. vision, audition), as they reflect both
the external environment and the internal state of the robot. The provided
multimodal dataset is expected to have multiple usages, such as multimodal
unsupervised object learning, multimodal prediction and egomotion/causality
detection.Comment: The CREATE dataset is Open access and available on IEEE Dataport
(https://ieee-dataport.org/open-access/create-multimodal-dataset-unsupervised-learning-and-generative-modeling-sensory-data
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization
This paper proposes a novel approach for extending monocular visual odometry
to a stereo camera system. The proposed method uses an additional camera to
accurately estimate and optimize the scale of the monocular visual odometry,
rather than triangulating 3D points from stereo matching. Specifically, the 3D
points generated by the monocular visual odometry are projected onto the other
camera of the stereo pair, and the scale is recovered and optimized by directly
minimizing the photometric error. It is computationally efficient, adding
minimal overhead to the stereo vision system compared to straightforward stereo
matching, and is robust to repetitive texture. Additionally, direct scale
optimization enables stereo visual odometry to be purely based on the direct
method. Extensive evaluation on public datasets (e.g., KITTI), and outdoor
environments (both terrestrial and underwater) demonstrates the accuracy and
efficiency of a stereo visual odometry approach extended by scale optimization,
and its robustness in environments with challenging textures
Fusion of Monocular Vision and Radio-based Ranging for Global Scale Estimation and Drift Mitigation
Monocular vision-based Simultaneous Localization and Mapping (SLAM) is used
for various purposes due to its advantages in cost, simple setup, as well as
availability in the environments where navigation with satellites is not
effective. However, camera motion and map points can be estimated only up to a
global scale factor with monocular vision. Moreover, estimation error
accumulates over time without bound, if the camera cannot detect the previously
observed map points for closing a loop. We propose an innovative approach to
estimate a global scale factor and reduce drifts in monocular vision-based
localization with an additional single ranging link. Our method can be easily
integrated with the back-end of monocular visual SLAM methods. We demonstrate
our algorithm with real datasets collected on a rover, and show the evaluation
results.Comment: Submitted to the International Conference on Robotics and Automation
(ICRA) 201
Keyframe-based Direct Thermal-Inertial Odometry
This paper proposes an approach for fusing direct radiometric data from a
thermal camera with inertial measurements to extend the robotic capabilities of
aerial robots for navigation in GPS-denied and visually degraded environments
in the conditions of darkness and in the presence of airborne obscurants such
as dust, fog and smoke. An optimization based approach is developed that
jointly minimizes the re-projection error of 3D landmarks and inertial
measurement errors. The developed solution is extensively verified against both
ground-truth in an indoor laboratory setting, as well as inside an underground
mine under severely visually degraded conditions.Comment: 7 pages, 8 figures, Accepted at International Conference on Robotics
and Automation (ICRA) 201
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