35,119 research outputs found
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent
localization in environmental fields characterized by spatially correlated
measurements. This paper presents a Gaussian process localization (GP-Localize)
algorithm that, in contrast to existing works, can exploit the spatially
correlated field measurements taken during a robot's exploration (instead of
relying on prior training data) for efficiently and scalably learning the GP
observation model online through our proposed novel online sparse GP. As a
result, GP-Localize is capable of achieving constant time and memory (i.e.,
independent of the size of the data) per filtering step, which demonstrates the
practical feasibility of using GPs for persistent robot localization and
autonomy. Empirical evaluation via simulated experiments with real-world
datasets and a real robot experiment shows that GP-Localize outperforms
existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended
version with proofs, 10 page
Local Ranking Problem on the BrowseGraph
The "Local Ranking Problem" (LRP) is related to the computation of a
centrality-like rank on a local graph, where the scores of the nodes could
significantly differ from the ones computed on the global graph. Previous work
has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a
graph where nodes are webpages and edges are browsing transitions. Recently,
this graph has received more and more attention in many different tasks such as
ranking, prediction and recommendation. However, a web-server has only the
browsing traffic performed on its pages (local BrowseGraph) and, as a
consequence, the local computation can lead to estimation errors, which hinders
the increasing number of applications in the state of the art. Also, although
the divergence between the local and global ranks has been measured, the
possibility of estimating such divergence using only local knowledge has been
mainly overlooked. These aspects are of great interest for online service
providers who want to: (i) gauge their ability to correctly assess the
importance of their resources only based on their local knowledge, and (ii)
take into account real user browsing fluxes that better capture the actual user
interest than the static hyperlink network. We study the LRP problem on a
BrowseGraph from a large news provider, considering as subgraphs the
aggregations of browsing traces of users coming from different domains. We show
that the distance between rankings can be accurately predicted based only on
structural information of the local graph, being able to achieve an average
rank correlation as high as 0.8
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