12,538 research outputs found
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
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
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
Viewfinder: final activity report
The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources.
The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation.
The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
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