956 research outputs found
Gesture Recognition Aplication based on Dynamic Time Warping (DTW) FOR Omni-Wheel Mobile Robot
This project presents of the movement of omni-wheel robot moves in the trajectory obtained from the gesture recognition system based on Dynamic Time Warping. Single camera is used as the input of the system, which is also a reference to the movement of the omni-wheel robot. Some
systems for gesture recognition have been developed using various methods and different approaches. The movement of the omni-wheel robot using the method of Dynamic Time Wrapping (DTW) which has the advantage able to calculate the distance of two data vectors with different lengths. By using this method we can measure the similarity between two sequences at different times and speeds. Dynamic Time
Warping to compare the two parameters at varying times and speeds. Application of DTW widely applied in video, audio, graphics, etc. Due to data that can be changed in a linear manner so that it can be analyzed with DTW. In short can find the most suitable value by minimizing the difference between two multidimensional signals that have been compressed. DTW method is expected to gesture recognition
system to work optimally, have a high enough value of accuracy and processing time is realtime
Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks
This paper proposes a novel system to estimate and track the 3D poses of
multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D
pose of each person is computed by a central node which receives the
single-view outcomes from each camera of the network. Each single-view outcome
is computed by using a CNN for 2D pose estimation and extending the resulting
skeletons to 3D by means of the sensor depth. The proposed system is
marker-less, multi-person, independent of background and does not make any
assumption on people appearance and initial pose. The system provides real-time
outcomes, thus being perfectly suited for applications requiring user
interaction. Experimental results show the effectiveness of this work with
respect to a baseline multi-view approach in different scenarios. To foster
research and applications based on this work, we released the source code in
OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and
Automatio
Calibration by correlation using metric embedding from non-metric similarities
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just
by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time
correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then
the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to
formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on
the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional
scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?)
and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature)
as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case,
on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric
measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric
information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional),
and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm
performs as theoretically predicted for all corner cases of the observability analysis
Omni-directional catadioptric vision for soccer robots
This paper describes the design of a multi-part mirror catadioptric vision system and its use for self-localization and
detection of relevant objects in soccer robots. The mirror and associated algorithms have been used in robots participating in
the middle-size league of RoboCup â The World Cup of Soccer Robots.This work was supported by grant PRAXIS XXI BM/21091/99 of the Portuguese Foundation for Science and Technolog
GelSight360: An Omnidirectional Camera-Based Tactile Sensor for Dexterous Robotic Manipulation
Camera-based tactile sensors have shown great promise in enhancing a robot's
ability to perform a variety of dexterous manipulation tasks. Advantages of
their use can be attributed to the high resolution tactile data and 3D depth
map reconstructions they can provide. Unfortunately, many of these tactile
sensors use either a flat sensing surface, sense on only one side of the
sensor's body, or have a bulky form-factor, making it difficult to integrate
the sensors with a variety of robotic grippers. Of the camera-based sensors
that do have all-around, curved sensing surfaces, many cannot provide 3D depth
maps; those that do often require optical designs specified to a particular
sensor geometry. In this work, we introduce GelSight360, a fingertip-like,
omnidirectional, camera-based tactile sensor capable of producing depth maps of
objects deforming the sensor's surface. In addition, we introduce a novel
cross-LED lighting scheme that can be implemented in different all-around
sensor geometries and sizes, allowing the sensor to easily be reconfigured and
attached to different grippers of varying DOFs. With this work, we enable
roboticists to quickly and easily customize high resolution tactile sensors to
fit their robotic system's needs
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