17,273 research outputs found
Fast and Robust Detection of Fallen People from a Mobile Robot
This paper deals with the problem of detecting fallen people lying on the
floor by means of a mobile robot equipped with a 3D depth sensor. In the
proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
is over-segmented into small patches. Fallen people are then detected by means
of two SVM classifiers: the first one labels each patch, while the second one
captures the spatial relations between them. This novel approach showed to be
robust and fast. Indeed, thanks to the use of small patches, fallen people in
real cluttered scenes with objects side by side are correctly detected.
Moreover, the algorithm can be executed on a mobile robot fitted with a
standard laptop making it possible to exploit the 2D environmental map built by
the robot and the multiple points of view obtained during the robot navigation.
Additionally, this algorithm is robust to illumination changes since it does
not rely on RGB data but on depth data. All the methods have been thoroughly
validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
as a further contribution. It consists of several static and dynamic sequences
with 15 different people and 2 different environments
Teaching humanoid robotics by means of human teleoperation through RGB-D sensors
This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels
Recognizing Objects In-the-wild: Where Do We Stand?
The ability to recognize objects is an essential skill for a robotic system
acting in human-populated environments. Despite decades of effort from the
robotic and vision research communities, robots are still missing good visual
perceptual systems, preventing the use of autonomous agents for real-world
applications. The progress is slowed down by the lack of a testbed able to
accurately represent the world perceived by the robot in-the-wild. In order to
fill this gap, we introduce a large-scale, multi-view object dataset collected
with an RGB-D camera mounted on a mobile robot. The dataset embeds the
challenges faced by a robot in a real-life application and provides a useful
tool for validating object recognition algorithms. Besides describing the
characteristics of the dataset, the paper evaluates the performance of a
collection of well-established deep convolutional networks on the new dataset
and analyzes the transferability of deep representations from Web images to
robotic data. Despite the promising results obtained with such representations,
the experiments demonstrate that object classification with real-life robotic
data is far from being solved. Finally, we provide a comparative study to
analyze and highlight the open challenges in robot vision, explaining the
discrepancies in the performance
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