25,870 research outputs found
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
We address one of the main challenges towards autonomous quadrotor flight in
complex environments, which is flight through narrow gaps. While previous works
relied on off-board localization systems or on accurate prior knowledge of the
gap position and orientation, we rely solely on onboard sensing and computing
and estimate the full state by fusing gap detection from a single onboard
camera with an IMU. This problem is challenging for two reasons: (i) the
quadrotor pose uncertainty with respect to the gap increases quadratically with
the distance from the gap; (ii) the quadrotor has to actively control its
orientation towards the gap to enable state estimation (i.e., active vision).
We solve this problem by generating a trajectory that considers geometric,
dynamic, and perception constraints: during the approach maneuver, the
quadrotor always faces the gap to allow state estimation, while respecting the
vehicle dynamics; during the traverse through the gap, the distance of the
quadrotor to the edges of the gap is maximized. Furthermore, we replan the
trajectory during its execution to cope with the varying uncertainty of the
state estimate. We successfully evaluate and demonstrate the proposed approach
in many real experiments. To the best of our knowledge, this is the first work
that addresses and achieves autonomous, aggressive flight through narrow gaps
using only onboard sensing and computing and without prior knowledge of the
pose of the gap
Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking
The fruit fly Drosophila melanogaster is a widely used model organism in studies of genetics, developmental biology and biomechanics. One limitation for exploiting Drosophila as a model system for behavioral neurobiology is that measuring body kinematics during behavior is labor intensive and subjective. In order to quantify flight kinematics during different types of maneuvers, we have developed a visual tracking system that estimates the posture of the fly from multiple calibrated cameras. An accurate geometric fly model is designed using unit quaternions to capture complex body and wing rotations, which are automatically fitted to the images in each time frame. Our approach works across a range of flight behaviors, while also being robust to common environmental clutter. The tracking system is used in this paper to compare wing and body motion during both voluntary and escape take-offs. Using our automated algorithms, we are able to measure stroke amplitude, geometric angle of attack and other parameters important to a mechanistic understanding of flapping flight. When compared with manual tracking methods, the algorithm estimates body position within 4.4±1.3% of the body length, while body orientation is measured within 6.5±1.9 deg. (roll), 3.2±1.3 deg. (pitch) and 3.4±1.6 deg. (yaw) on average across six videos. Similarly, stroke amplitude and deviation are estimated within 3.3 deg. and 2.1 deg., while angle of attack is typically measured within 8.8 deg. comparing against a human digitizer. Using our automated tracker, we analyzed a total of eight voluntary and two escape take-offs. These sequences show that Drosophila melanogaster do not utilize clap and fling during take-off and are able to modify their wing kinematics from one wingstroke to the next. Our approach should enable biomechanists and ethologists to process much larger datasets than possible at present and, therefore, accelerate insight into the mechanisms of free-flight maneuvers of flying insects
Tightly Coupled GNSS and Vision Navigation for Unmanned Air Vehicle Applications
This paper explores the unique benefits that can be obtained from a tight integration of a GNSS sensor and a forward-looking vision sensor. The motivation of this research is the belief that both GNSS and vision will be integral features of future UAV avionics architectures, GNSS for basic aircraft navigation and vision for obstacle-aircraft collision avoidance. The paper will show that utilising basic single-antenna GNSS measurements and observables, along with aircraft information derived from optical flow techniques creates unique synergies. Results of the accuracy of attitude estimates will be presented, based a comprehensive Matlab® Simulink® model which re-creates an optical flow stream based on the flight of an aircraft. This paper establishes the viability of this novel integrated GNSS/Vision approach for use as the complete UAV sensor package, or as a backup sensor for an inertial navigation system
Effective Target Aware Visual Navigation for UAVs
In this paper we propose an effective vision-based navigation method that
allows a multirotor vehicle to simultaneously reach a desired goal pose in the
environment while constantly facing a target object or landmark. Standard
techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual
Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast
maneuvers) do not allow to constantly maintain the line of sight with a target
of interest. Instead, we compute the optimal trajectory by solving a non-linear
optimization problem that minimizes the target re-projection error while
meeting the UAV's dynamic constraints. The desired trajectory is then tracked
by means of a real-time Non-linear Model Predictive Controller (NMPC): this
implicitly allows the multirotor to satisfy both the required constraints. We
successfully evaluate the proposed approach in many real and simulated
experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR)
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Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. These cameras do not
suffer from motion blur and have a very high dynamic range, which enables them
to provide reliable visual information during high speed motions or in scenes
characterized by high dynamic range. However, event cameras output only little
information when the amount of motion is limited, such as in the case of almost
still motion. Conversely, standard cameras provide instant and rich information
about the environment most of the time (in low-speed and good lighting
scenarios), but they fail severely in case of fast motions, or difficult
lighting such as high dynamic range or low light scenes. In this paper, we
present the first state estimation pipeline that leverages the complementary
advantages of these two sensors by fusing in a tightly-coupled manner events,
standard frames, and inertial measurements. We show on the publicly available
Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement
of 130% over event-only pipelines, and 85% over standard-frames-only
visual-inertial systems, while still being computationally tractable.
Furthermore, we use our pipeline to demonstrate - to the best of our knowledge
- the first autonomous quadrotor flight using an event camera for state
estimation, unlocking flight scenarios that were not reachable with traditional
visual-inertial odometry, such as low-light environments and high-dynamic range
scenes.Comment: 8 pages, 9 figures, 2 table
Cross-calibration of Time-of-flight and Colour Cameras
Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table
Calibration of Viking imaging system pointing, image extraction, and optical navigation measure
Pointing control and knowledge accuracy of Viking Orbiter science instruments is controlled by the scan platform. Calibration of the scan platform and the imaging system was accomplished through mathematical models. The calibration procedure and results obtained for the two Viking spacecraft are described. Included are both ground and in-flight scan platform calibrations, and the additional calibrations unique to optical navigation
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