Authors:
Michael Sapienza
and
Kenneth Camilleri
Affiliation:
University of Malta, Malta
Keyword(s):
Traversability Detection, Autonomous Robotics, Self-guidance.
Related
Ontology
Subjects/Areas/Topics:
Human-Robots Interfaces
;
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
In order for robots to be integrated into human active spaces and perform useful tasks, they must be capable
of discriminating between traversable surfaces and obstacle regions in their surrounding environment. In
this work, a principled semi-supervised (EM) framework is presented for the detection of traversable image
regions for use on a low-cost monocular mobile robot. We propose a novel generative model for the occurrence
of traversability cues, which are a measure of dissimilarity between safe-window and image superpixel
features. Our classification results on both indoor and outdoor images sequences demonstrate its generality
and adaptability to multiple environments through the online learning of an exponential mixture model. We
show that this appearance-based vision framework is robust and can quickly and accurately estimate the probabilistic
traversability of an image using no temporal information. Moreover, the reduction in safe-window
size as compared to the state-of-the-a
rt enables a self-guided monocular robot to roam in closer proximity of
obstacles.
(More)