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
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
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 focuses on the study on the map fusion problem as one of the steps towards the independent multi-robot map building. The situation proposed considers a set of several robots performing map building tasks. 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.