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
Mathematical Programming: Series A and B
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Distributed Multi-Robot Exploration and Mapping
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
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
Map fusion in an independent multi-robot approach
WSEAS TRANSACTIONS on SYSTEMS
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This paper focusses on the study of the Map Alignment problem in a multirobot SLAM context. The map fusion problem can be tackled in two stages: map alignment and map merging. The alignment stage consists in obtaining the tranformation between the reference systems of the robots. Then, in the map merging stage, the maps built by different robots can be fused into a single one. In this paper, we concentrate on the alignment stage. Particularly, we have a team of robots, each one building its own local map independently. At some point, the fusion of the maps may be required. In this case, these maps must be aligned. We therefore evaluate a set of aligning methods. These methods establish correspondences between each pair of maps and compute an initial estimate of the alignment. Finally, we apply the least squares minimization to obtain a more accurate solution. Each robot extracts distinctive 3D points from the environment with a stereo camera. Then, the robots use these observations as landmarks to build their maps while simultaneously localize themselves. The SLAM problem is solved using the FastSLAM algorithm. The movements of the robots occur only in 2D plane, so although the maps are 3D landmark-based, the alignment takes place only in 2D.