Lifelong information-driven exploration to complete and refine 4-D spatio-temporal maps

Santos, Joao Machado, Krajnik, Tomas, Pulido Fentanes, Jaime and Duckett, Tom (2016) Lifelong information-driven exploration to complete and refine 4-D spatio-temporal maps. IEEE Robotics and Automation Letters, 1 (2). pp. 684-691. ISSN 2377-3766

22698 LRA2516594-public.pdf
22698 LRA2516594-public.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


This paper presents an exploration method that allows
mobile robots to build and maintain spatio-temporal models
of changing environments. The assumption of a perpetuallychanging
world adds a temporal dimension to the exploration
problem, making spatio-temporal exploration a never-ending,
life-long learning process. We address the problem by application
of information-theoretic exploration methods to spatio-temporal
models that represent the uncertainty of environment states as
probabilistic functions of time. This allows to predict the potential
information gain to be obtained by observing a particular area
at a given time, and consequently, to decide which locations to
visit and the best times to go there.
To validate the approach, a mobile robot was deployed
continuously over 5 consecutive business days in a busy office
environment. The results indicate that the robot’s ability to spot
environmental changes im

Keywords:Mapping, Service Robots, long-term autonomy, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:22698
Deposited On:24 Mar 2016 15:35

Repository Staff Only: item control page