Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the classification of views of piecewise smooth objects
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
3D Visual Odometry for Road Vehicles
Journal of Intelligent and Robotic Systems
ISTASC'06 Proceedings of the 6th WSEAS International Conference on Systems Theory & Scientific Computation
A Framework for Simulation and Testing of UAVs in Cooperative Scenarios
Journal of Intelligent and Robotic Systems
Improving simultaneous mapping and localization in 3D using global constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Multi-robot visual SLAM using a Rao-Blackwellized particle filter
Robotics and Autonomous Systems
Alignment of visual maps in multirobot FastSLAM
CIMMACS'09 Proceedings of the 8th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics
Multi-robot map alignment in visual SLAM
WSEAS TRANSACTIONS on SYSTEMS
A comparative evaluation of interest point detectors and local descriptors for visual SLAM
Machine Vision and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
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This paper concentrates on the study on the map fusion problem in the context of a multi-robot map building approach. Concretely it is seen as one of the steps towards the independent multi-robot map building. In the situation proposed a set of several robots performs map building tasks without the notion of other robots' existence. Each robot builds its own local map using its observations and estimates its path independently. As a result, there will be a set of local maps that can be fused into a global one. This is the case when the map fusion takes importance. Particularly, we focus our experiments on landmark-based maps constructed using visual information and by means of a particle filter. When fusing two maps, we consider the uncertainty of the landmarks integrated by each different robot to its map.