Authors:
Eduardo Fernández-Moral
;
Javier Gonzalez-Jimenez
and
Vicente Arévalo
Affiliation:
University of Málaga, Spain
Keyword(s):
Monocular SLAM, Metric-topological Map, Map Partitioning.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vision, Recognition and Reconstruction
Abstract:
In the last very few years, monocular SLAM approaches based on bundle adjustment are achieving amazing results in terms of accuracy, computational efficiency, and density of the map. When such solutions are applied on large scenarios it is crucial for the system scalability to maintain a map representation that permits efficient map optimization and augmentation. In order to cope with such large maps, we present an on-the-fly partitioning technique which allows abstraction from the metric map to operate more efficiently. The result is a metric-topological arrangement where the areas with highly-connected observations are grouped in submaps weakly interconnected to each other. This is accomplished by progressively cutting a graph representation of the map, where the nodes are keyframes and the arcs between them represent their shared observations. The experimental results indicate that the proposed approach improves the efficiency of monocular SLAM and provides a metric-topological wo
rld representation suitable for other robotic tasks.
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