8 research outputs found
Photogrammetry System and Method for Determining Relative Motion Between Two Bodies
A photogrammetry system and method provide for determining the relative position between two objects. The system utilizes one or more imaging devices, such as high speed cameras, that are mounted on a first body, and three or more photogrammetry targets of a known location on a second body. The system and method can be utilized with cameras having fish-eye, hyperbolic, omnidirectional, or other lenses. The system and method do not require overlapping fields-of-view if two or more cameras are utilized. The system and method derive relative orientation by equally weighting information from an arbitrary number of heterogeneous cameras, all with non-overlapping fields-of-view. Furthermore, the system can make the measurements with arbitrary wide-angle lenses on the cameras
Line-constrained camera location estimation in multi-image stereomatching
Stereomatching is an effective way of acquiring dense depth information from a scene when active measurements are not possible. So-called lightfield methods take a snapshot from many camera locations along a defined trajectory (usually uniformly linear or on a regular grid—we will assume a linear trajectory) and use this information to compute accurate depth estimates. However, they require the locations for each of the snapshots to be known: the disparity of an object between images is related to both the distance of the camera to the object and the distance between the camera positions for both images. Existing solutions use sparse feature matching for camera location estimation. In this paper, we propose a novel method that uses dense correspondences to do the same, leveraging an existing depth estimation framework to also yield the camera locations along the line. We illustrate the effectiveness of the proposed technique for camera location estimation both visually for the rectification of epipolar plane images and quantitatively with its effect on the resulting depth estimation. Our proposed approach yields a valid alternative for sparse techniques, while still being executed in a reasonable time on a graphics card due to its highly parallelizable nature
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
Visual control of multi-rotor UAVs
Recent miniaturization of computer hardware, MEMs sensors, and high energy density
batteries have enabled highly capable mobile robots to become available at low cost.
This has driven the rapid expansion of interest in multi-rotor unmanned aerial vehicles.
Another area which has expanded simultaneously is small powerful computers, in the
form of smartphones, which nearly always have a camera attached, many of which now
contain a OpenCL compatible graphics processing units. By combining the results of
those two developments a low-cost multi-rotor UAV can be produced with a low-power
onboard computer capable of real-time computer vision. The system should also use
general purpose computer vision software to facilitate a variety of experiments.
To demonstrate this I have built a quadrotor UAV based on control hardware from
the Pixhawk project, and paired it with an ARM based single board computer, similar
those in high-end smartphones. The quadrotor weights 980 g and has a flight time of
10 minutes. The onboard computer capable of running a pose estimation algorithm
above the 10 Hz requirement for stable visual control of a quadrotor.
A feature tracking algorithm was developed for efficient pose estimation, which relaxed
the requirement for outlier rejection during matching. Compared with a RANSAC-
only algorithm the pose estimates were less variable with a Z-axis standard deviation
0.2 cm compared with 2.4 cm for RANSAC. Processing time per frame was also faster
with tracking, with 95 % confidence that tracking would process the frame within 50 ms,
while for RANSAC the 95 % confidence time was 73 ms. The onboard computer ran the
algorithm with a total system load of less than 25 %. All computer vision software uses
the OpenCV library for common computer vision algorithms, fulfilling the requirement
for running general purpose software.
The tracking algorithm was used to demonstrate the capability of the system by per-
forming visual servoing of the quadrotor (after manual takeoff). Response to external
perturbations was poor however, requiring manual intervention to avoid crashing. This
was due to poor visual controller tuning, and to variations in image acquisition and
attitude estimate timing due to using free running image acquisition.
The system, and the tracking algorithm, serve as proof of concept that visual control of
a quadrotor is possible using small low-power computers and general purpose computer
vision software
Seamless Positioning and Navigation in Urban Environment
L'abstract è presente nell'allegato / the abstract is in the attachmen
NASA Tech Briefs, November 2002
Topics include: a technology focus on engineering materials, electronic components and systems, software, mechanics, machinery/automation, manufacturing, bio-medical, physical sciences, information sciences book and reports, and a special section of Photonics Tech Briefs
Visual and Camera Sensors
This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors