2,310 research outputs found
RSGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4
table
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the
sensors of choice for depth perception as they are typically cheaper and more
versatile than their active counterparts. Their biggest drawback, however, is
that they do not directly sense depth maps; instead, these must be estimated
through data-intensive processes. Therefore, appropriate algorithm selection
plays an important role in achieving the desired performance characteristics.
Motivated by applications in space and mobile robotics, we implement and
evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering
one of the best trade-offs between efficiency and accuracy, ELAS has only been
shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all
intriguing properties of the original algorithm, such as the slanted plane
priors, but can achieve a frame rate of 47fps whilst consuming under 4W of
power. Unlike previous FPGA based designs, we take advantage of both components
on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate
more complex and computationally diverse algorithms for such low power,
real-time systems.Comment: 8 pages, 7 figures, 2 table
A high speed Tri-Vision system for automotive applications
Purpose: Cameras are excellent ways of non-invasively monitoring the interior and exterior of vehicles. In particular, high speed stereovision and multivision systems are important for transport applications such as driver eye tracking or collision avoidance. This paper addresses the synchronisation problem which arises when multivision camera systems are used to capture the high speed motion common in such applications.
Methods: An experimental, high-speed tri-vision camera system intended for real-time driver eye-blink and saccade measurement was designed, developed, implemented and tested using prototype, ultra-high dynamic range, automotive-grade image sensors specifically developed by E2V (formerly Atmel) Grenoble SA as part of the European FP6 project – sensation (advanced sensor development for attention stress, vigilance and sleep/wakefulness monitoring).
Results : The developed system can sustain frame rates of 59.8 Hz at the full stereovision resolution of 1280 × 480 but this can reach 750 Hz when a 10 k pixel Region of Interest (ROI) is used, with a maximum global shutter speed of 1/48000 s and a shutter efficiency of 99.7%. The data can be reliably transmitted uncompressed over standard copper Camera-Link® cables over 5 metres. The synchronisation error between the left and right stereo images is less than 100 ps and this has been verified both electrically and optically. Synchronisation is automatically established at boot-up and maintained during resolution changes. A third camera in the set can be configured independently. The dynamic range of the 10bit sensors exceeds 123 dB with a spectral sensitivity extending well into the infra-red range.
Conclusion: The system was subjected to a comprehensive testing protocol, which confirms that the salient requirements for the driver monitoring application are adequately met and in some respects, exceeded. The synchronisation technique presented may also benefit several other automotive stereovision applications including near and far-field obstacle detection and collision avoidance, road condition monitoring and others.Partially funded by the EU FP6 through the IST-507231 SENSATION project.peer-reviewe
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 2016; 8 pages, 5 figure
Real-time motor rotation frequency detection with event-based visual and spike-based auditory AER sensory integration for FPGA
Multisensory integration is commonly
used in various robotic areas to collect more
environmental information using different and
complementary types of sensors. Neuromorphic
engineers mimics biological systems behavior to
improve systems performance in solving engineering
problems with low power consumption. This work
presents a neuromorphic sensory integration scenario
for measuring the rotation frequency of a motor using
an AER DVS128 retina chip (Dynamic Vision Sensor)
and a stereo auditory system on a FPGA completely
event-based. Both of them transmit information with
Address-Event-Representation (AER). This
integration system uses a new AER monitor hardware
interface, based on a Spartan-6 FPGA that allows two
operational modes: real-time (up to 5 Mevps through
USB2.0) and data logger mode (up to 20Mevps for
33.5Mev stored in onboard DDR RAM). The sensory
integration allows reducing prediction error of the
rotation speed of the motor since audio processing
offers a concrete range of rpm, while DVS can be
much more accurate.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0
Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
In this paper, we explore the capabilities of a sound
classification system that combines both a novel FPGA cochlear
model implementation and a bio-inspired technique based on a
trained convolutional spiking network. The neuromorphic
auditory system that is used in this work produces a form of
representation that is analogous to the spike outputs of the
biological cochlea. The auditory system has been developed using
a set of spike-based processing building blocks in the frequency
domain. They form a set of band pass filters in the spike-domain
that splits the audio information in 128 frequency channels, 64
for each of two audio sources. Address Event Representation
(AER) is used to communicate the auditory system with the
convolutional spiking network. A layer of convolutional spiking
network is developed and trained on a computer with the ability
to detect two kinds of sound: artificial pure tones in the presence
of white noise and electronic musical notes. After the training
process, the presented system is able to distinguish the different
sounds in real-time, even in the presence of white noise.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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