10,591 research outputs found
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
In order to improve usability and safety, modern unmanned aerial vehicles
(UAVs) are equipped with sensors to monitor the environment, such as
laser-scanners and cameras. One important aspect in this monitoring process is
to detect obstacles in the flight path in order to avoid collisions. Since a
large number of consumer UAVs suffer from tight weight and power constraints,
our work focuses on obstacle avoidance based on a lightweight stereo camera
setup. We use disparity maps, which are computed from the camera images, to
locate obstacles and to automatically steer the UAV around them. For disparity
map computation we optimize the well-known semi-global matching (SGM) approach
for the deployment on an embedded FPGA. The disparity maps are then converted
into simpler representations, the so called U-/V-Maps, which are used for
obstacle detection. Obstacle avoidance is based on a reactive approach which
finds the shortest path around the obstacles as soon as they have a critical
distance to the UAV. One of the fundamental goals of our work was the reduction
of development costs by closing the gap between application development and
hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for
porting our algorithms, which are written in C/C++, to the embedded FPGA. We
evaluated our implementation of the disparity estimation on the KITTI Stereo
2015 benchmark. The integrity of the overall realtime reactive obstacle
avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in
conjunction with two flight simulators.Comment: Accepted in the International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Scienc
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
Miniaturized embedded stereo vision system (MESVS)
Stereo vision is one of the fundamental problems of computer vision. It is also one of the oldest and heavily investigated areas of 3D vision. Recent advances of stereo matching methodologies and availability of high performance and efficient algorithms along with availability of fast and affordable hardware technology, have allowed researchers to develop several stereo vision systems capable of operating at real-time. Although a multitude of such systems exist in the literature, the majority of them concentrates only on raw performance and quality rather than factors such as dimension, and power requirement, which are of significant importance in the embedded settings.
In this thesis a new miniaturized embedded stereo vision system (MESVS) is presented, which is miniaturized to fit within a package of 5x5cm, is power efficient, and cost-effective. Furthermore, through application of embedded programming techniques and careful optimization, MESVS achieves the real-time performance of 20 frames per second. This work discusses the various challenges involved regarding design and implementation of this system and the measures taken to tackle them
Adaptive SLAM with synthetic stereo dataset generation for real-time dense 3D reconstruction
International audienceIn robotic mapping and navigation, of prime importance today with the trend for autonomous cars, simultaneous localization and mapping (SLAM) algorithms often use stereo vision to extract 3D information of the surrounding world. Whereas the number of creative methods for stereo-based SLAM is continuously increasing, the variety of datasets is relatively poor and the size of their contents relatively small. This size issue is increasingly problematic, with the recent explosion of deep learning based approaches, several methods require an important amount of data. Those multiple techniques contribute to enhance the precision of both localization estimation and mapping estimation to a point where the accuracy of the sensors used to get the ground truth might be questioned. Finally, because today most of these technologies are embedded on on-board systems, the power consumption and real-time constraints turn to be key requirements. Our contribution is twofold: we propose an adaptive SLAM method that reduces the number of processed frame with minimum impact error, and we make available a synthetic flexible stereo dataset with absolute ground truth, which allows to run new benchmarks for visual odometry challenges. This dataset is available online at http://alastor.labri.fr/
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
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