3,365 research outputs found
Independent Motion Detection with Event-driven Cameras
Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for robots. Visual tracking, for example, is easily achieved
even for very fast stimuli, as only moving objects cause brightness changes.
However, cameras mounted on a moving robot are typically non-stationary and the
same tracking problem becomes confounded by background clutter events due to
the robot ego-motion. In this paper, we propose a method for segmenting the
motion of an independently moving object for event-driven cameras. Our method
detects and tracks corners in the event stream and learns the statistics of
their motion as a function of the robot's joint velocities when no
independently moving objects are present. During robot operation, independently
moving objects are identified by discrepancies between the predicted corner
velocities from ego-motion and the measured corner velocities. We validate the
algorithm on data collected from the neuromorphic iCub robot. We achieve a
precision of ~ 90 % and show that the method is robust to changes in speed of
both the head and the target.Comment: 7 pages, 6 figure
Control of free-flying space robot manipulator systems
New control techniques for self contained, autonomous free flying space robots were developed and tested experimentally. Free flying robots are envisioned as a key element of any successful long term presence in space. These robots must be capable of performing the assembly, maintenance, and inspection, and repair tasks that currently require human extravehicular activity (EVA). A set of research projects were developed and carried out using lab models of satellite robots and a flexible manipulator. The second generation space robot models use air cushion vehicle (ACV) technology to simulate in 2-D the drag free, zero g conditions of space. The current work is divided into 5 major projects: Global Navigation and Control of a Free Floating Robot, Cooperative Manipulation from a Free Flying Robot, Multiple Robot Cooperation, Thrusterless Robotic Locomotion, and Dynamic Payload Manipulation. These projects are examined in detail
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
Current approaches for visual-inertial odometry (VIO) are able to attain
highly accurate state estimation via nonlinear optimization. However, real-time
optimization quickly becomes infeasible as the trajectory grows over time, this
problem is further emphasized by the fact that inertial measurements come at
high rate, hence leading to fast growth of the number of variables in the
optimization. In this paper, we address this issue by preintegrating inertial
measurements between selected keyframes into single relative motion
constraints. Our first contribution is a \emph{preintegration theory} that
properly addresses the manifold structure of the rotation group. We formally
discuss the generative measurement model as well as the nature of the rotation
noise and derive the expression for the \emph{maximum a posteriori} state
estimator. Our theoretical development enables the computation of all necessary
Jacobians for the optimization and a-posteriori bias correction in analytic
form. The second contribution is to show that the preintegrated IMU model can
be seamlessly integrated into a visual-inertial pipeline under the unifying
framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a \emph{structureless} model
for visual measurements, which avoids optimizing over the 3D points, further
accelerating the computation. We perform an extensive evaluation of our
monocular \VIO pipeline on real and simulated datasets. The results confirm
that our modelling effort leads to accurate state estimation in real-time,
outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions
on Robotics (TRO) 201
Satellite Servicing's Autonomous Rendezvous and Docking Testbed on the International Space Station
The Space Servicing Capabilities Project (SSCP) at NASA's Goddard Space Flight Center (GSFC) has been tasked with developing systems for servicing space assets. Starting in 2009, the SSCP completed a study documenting potential customers and the business case for servicing, as well as defining several notional missions and required technologies. In 2010, SSCP moved to the implementation stage by completing several ground demonstrations and commencing development of two International Space Station (ISS) payloads-the Robotic Refueling Mission (RRM) and the Dextre Pointing Package (DPP)--to mitigate new technology risks for a robotic mission to service existing assets in geosynchronous orbit. This paper introduces the DPP, scheduled to fly in July of 2012 on the third operational SpaceX Dragon mission, and its Autonomous Rendezvous and Docking (AR&D) instruments. The combination of sensors and advanced avionics provide valuable on-orbit demonstrations of essential technologies for servicing existing vehicles, both cooperative and non-cooperative
Autonomous aerial robot for high-speed search and intercept applications
In recent years, high-speed navigation and environment interaction in the context of
aerial robotics has become a field of interest for several academic and industrial research studies. In
particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research
area due to their potential usability in several environments. Nevertheless, SaI tasks involve a
challenging development regarding sensory weight, onboard computation resources, actuation design,
and algorithms for perception and control, among others. In this work, a fully autonomous aerial
robot for high-speed object grasping has been proposed. As an additional subtask, our system is able
to autonomously pierce balloons located in poles close to the surface. Our first contribution is the
design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with
additional sensors enabling the robot to grasp objects at high speeds. The second contribution is
a complete software framework consisting of perception, state estimation, motion planning, motion
control, and mission control in order to rapidly and robustly perform the autonomous grasping
mission. Our approach has been validated in a challenging international competition and has shown
outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s
in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit
Accurate Long-Term Multiple People Tracking Using Video and Body-Worn IMUs
Most modern approaches for video-based multiple people tracking rely on human appearance to exploit similarities between person detections. Consequently, tracking accuracy degrades if this kind of information is not discriminative or if people change apparel. In contrast, we present a method to fuse video information with additional motion signals from body-worn inertial measurement units (IMUs). In particular, we propose a neural network to relate person detections with IMU orientations, and formulate a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. The fusion of visual and inertial cues provides several advantages. The association of detection boxes in the video and IMU devices is based on motion, which is independent of a person's outward appearance. Furthermore, inertial sensors provide motion information irrespective of visual occlusions. Hence, once detections in the video are associated with an IMU device, intermediate positions can be reconstructed from corresponding inertial sensor data, which would be unstable using video only. Since no dataset exists for this new setting, we release a dataset of challenging tracking sequences, containing video and IMU recordings together with ground-truth annotations. We evaluate our approach on our new dataset, achieving an average IDF1 score of 91.2%. The proposed method is applicable to any situation that allows one to equip people with inertial sensors. © 1992-2012 IEEE
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