2,781 research outputs found
An Evaluation of Game Usability in Shared Mixed and Virtual Environments
Augmented reality (AR) and virtual reality (VR) technologies are increasingly becoming more pervasive and important in the information technology area. Thanks to the technological improvements, desktop interfaces are being replaced by immersive VR devices that offer a more compelling game experience. AR games have started to draw attention of researchers and companies for their ability to exploit both the real and virtual environments. New fascinating challenges are generated by the possibility of designing hybrid games that allow several users to access shared environments exploiting the features of both AR and VR devices. However, the user experience and usability can be affected by several parameters, such as the field of view (FoV) of the employed devices or the realism of the scene. The work presented in this chapter aims to assess the impact of the FoV on the usability of the interfaces in a first-person shooter game. Two players, interacting with AR (first player) and VR (second player) devices, can fight each other in a large game environment. Although we cannot ascertain that different FoVs have affected the game usability, users considered the narrow FoV interfaces to be less usable, even though they could freely move around the real environment
Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot
In this paper, we develop an online active mapping system to enable a
quadruped robot to autonomously survey large physical structures. We describe
the perception, planning and control modules needed to scan and reconstruct an
object of interest, without requiring a prior model. The system builds a voxel
representation of the object, and iteratively determines the Next-Best-View
(NBV) to extend the representation, according to both the reconstruction itself
and to avoid collisions with the environment. By computing the expected
information gain of a set of candidate scan locations sampled on the as-sensed
terrain map, as well as the cost of reaching these candidates, the robot
decides the NBV for further exploration. The robot plans an optimal path
towards the NBV, avoiding obstacles and un-traversable terrain. Experimental
results on both simulated and real-world environments show the capability and
efficiency of our system. Finally we present a full system demonstration on the
real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade
and an industrial structure
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
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