Localization and Mapping
Metric-topological maps for large scale monocular SLAM (ICINCO 2013)
Monocular SLAM approaches based on bundle adjustment have achieved 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 world representation suitable for other robotic tasks.
*This work received the "Best Student Paper Award" in the International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013).
Place recognition and localization in PbMaps (ICRA 2013)
Recently we have presented a new method for recognizing places in indoor environments based on the extraction of planar regions from range data provided by a hand-held RGB-D sensor. We propose to build a plane-based map (PbMap) consisting of a set of 3D planar patches described by simple geometric features (normal vector, centroid, area, etc.). This world representation is organized as a graph where the nodes represent the planar patches and the edges connect planes that are close by. This map structure permits to efficiently select subgraphs representing the local neighborhood of observed planes, that will be compared with other subgraphs corresponding to local neighborhoods of planes acquired previously. To find a candidate match between two subgraphs we employ an interpretation tree that permits working with partially observed and missing planes. The candidates from the interpretation tree are further checked out by a rigid registration test, which also gives us the relative pose between the matched places. The experimental results indicate that the proposed approach is an efficient way to solve this problem, working satisfactorily even when there are substantial changes in the scene (lifelong maps).