3,601 research outputs found
Robot navigation control based on monocular images: An image processing algorithm for obstacle avoidance decisions
This paper covers the use of monocular vision to control autonomous navigation for a robot in a dynamically changing environment. The solution focused on using colour segmentation against a selected floor plane to distinctly separate obstacles from traversable space, this is then supplemented with canny edge detection to separate similarly coloured boundaries to the floor plane. The resulting binary map (where white identifies an obstacle-free area and black identifies an obstacle) could then be processed by fuzzy logic or neural networks to control the robot’s next movements. Findings shows that the algorithm performed strongly on solid coloured carpets, wooden and concrete floors but had difficulty in separating colours in multi-coloured floor types such as patterned carpets
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
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