39,059 research outputs found
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Lazier Than Lazy Greedy
Is it possible to maximize a monotone submodular function faster than the
widely used lazy greedy algorithm (also known as accelerated greedy), both in
theory and practice? In this paper, we develop the first linear-time algorithm
for maximizing a general monotone submodular function subject to a cardinality
constraint. We show that our randomized algorithm, STOCHASTIC-GREEDY, can
achieve a approximation guarantee, in expectation, to the
optimum solution in time linear in the size of the data and independent of the
cardinality constraint. We empirically demonstrate the effectiveness of our
algorithm on submodular functions arising in data summarization, including
training large-scale kernel methods, exemplar-based clustering, and sensor
placement. We observe that STOCHASTIC-GREEDY practically achieves the same
utility value as lazy greedy but runs much faster. More surprisingly, we
observe that in many practical scenarios STOCHASTIC-GREEDY does not evaluate
the whole fraction of data points even once and still achieves
indistinguishable results compared to lazy greedy.Comment: In Proc. Conference on Artificial Intelligence (AAAI), 201
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