Paper Info
Title
Constrained optimal selection for multi-sensor robot navigation using plug-and-play factor graphs
Abstract
This paper proposes a real-time navigation approach that is able to integrate many sensor types while fulfilling performance needs and system constraints. Our approach uses a plug-and-play factor graph framework, which extends factor graph formulation to encode sensor measurements with different frequencies, latencies, and noise distributions. It provides a flexible foundation for plug-and-play sensing, and can incorporate new evolving sensors. A novel constrained optimal selection mechanism is presented to identify the optimal subset of active sensors to use, during initialization and when any sensor condition changes. This mechanism constructs candidate subsets of sensors based on heuristic rules and a ternary tree expansion algorithm. It quickly decides the optimal subset among candidates by maximizing observability coverage on state variables, while satisfying resource constraints and accuracy demands. Experimental results demonstrate that our approach selects subsets of sensors to provide satisfactory navigation solutions under various conditions, on large-scale real data sets using many sensors.
Year
DOI
Venue
2014
10.1109/ICRA.2014.6906925
ICRA
Keywords
DocType
Volume
multisensor robot navigation,active sensors,multi-robot systems,factor graph formulation,ternary tree expansion algorithm,real-time navigation approach,novel constrained optimal selection mechanism,navigation,observability coverage,graph theory,heuristic rules,sensors,plug-and-play factor graph framework
Conference
2014
Issue
ISSN
Citations 
1
1050-4729
8
PageRank 
References 
Authors
0.49
10
6
Name
Order
Citations
PageRank
Han-Pang Chiu19410.83
Xun S. Zhou280.83
Luca Carlone364842.93
Frank Dellaert45242438.33
Supun Samarasekera579285.72
Rakesh Kumar 00016162.52