2,299 research outputs found
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages
with 9 figure
AlphaPilot: Autonomous Drone Racing
This paper presents a novel system for autonomous, vision-based drone racing
combining learned data abstraction, nonlinear filtering, and time-optimal
trajectory planning. The system has successfully been deployed at the first
autonomous drone racing world championship: the 2019 AlphaPilot Challenge.
Contrary to traditional drone racing systems, which only detect the next gate,
our approach makes use of any visible gate and takes advantage of multiple,
simultaneous gate detections to compensate for drift in the state estimate and
build a global map of the gates. The global map and drift-compensated state
estimate allow the drone to navigate through the race course even when the
gates are not immediately visible and further enable to plan a near
time-optimal path through the race course in real time based on approximate
drone dynamics. The proposed system has been demonstrated to successfully guide
the drone through tight race courses reaching speeds up to 8m/s and ranked
second at the 2019 AlphaPilot Challenge.Comment: Accepted at Robotics: Science and Systems 2020, associated video at
https://youtu.be/DGjwm5PZQT
Towards Intuitive HMI for UAV Control
In the last decade, UAVs have become a widely used technology. As they are
used by both professionals and amateurs, there is a need to explore different
control modalities to make control intuitive and easier, especially for new
users. In this work, we compared the most widely used joystick control with a
custom human pose control. We used human pose estimation and arm movements to
send UAV commands in the same way that operators use their fingers to send
joystick commands. Experiments were conducted in a simulation environment with
first-person visual feedback. Participants had to traverse the same maze with
joystick and human pose control. Participants' subjective experience was
assessed using the raw NASA Task Load Index.Comment: 2022 International Conference on Smart Systems and Technologies (SST
Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study.
Accurate models of proprioceptive neural patterns could one day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimisation) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modelling. This paper uses and proposes a number of approximations and optimisations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed
Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time
Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust.
More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression.
Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time
Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust.
More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression.
Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
An intelligent robot for helping astronauts
This paper describes the development status of a prototype supervised intelligent robot for space application for purposes of (1) helping the crew of a spacecraft such as the Space Station with various tasks, such as holding objects and retrieving/replacing tools and other objects from/into storage, and (2) for purposes of retrieving detached objects, such as equipment or crew, that have become separated from their spacecraft. In addition to this set of tasks in this low-Earth-orbiting spacecraft environment, it is argued that certain aspects of the technology can be viewed as generic in approach, thereby offering insight into intelligent robots for other tasks and environments. Candidate software architectures and their key technical issues which enable real work in real environments to be accomplished safely and robustly are addressed. Results of computer simulations of grasping floating objects are presented. Also described are characterization results on the usable reduced gravity environment in an aircraft flying parabola (to simulate weightlessness) and results on hardware performance there. These results show it is feasible to use that environment for evaluative testing of dexterous grasping based on real-time vision of freely rotating and translating objects
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