Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Improving simultaneous mapping and localization in 3D using global constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Monte carlo localization using SIFT features
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
A hybrid solution to the multi-robot integrated exploration problem
Engineering Applications of Artificial Intelligence
Map fusion in an independent multi-robot approach
WSEAS TRANSACTIONS on SYSTEMS
The visual SLAM system for a hexapod robot
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
Distributed multi-camera visual mapping using topological maps of planar regions
Pattern Recognition
Towards multi-robot independent visual SLAM
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
Cooperative SLAM using M-Space representation of linear features
Robotics and Autonomous Systems
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This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem with a team of cooperative autonomous vehicles. We consider that each robot is equipped with a stereo camera and is able to observe visual landmarks in the environment. The SLAM approach presented here is feature-based, thus the map is represented by a set of 3D landmarks each one defined by a global position in space and a visual descriptor. The robots move independently along different trajectories and make relative measurements to landmarks in the environment in order to jointly build a common map using a Rao-Blackwellized particle filter. We show results obtained in a simulated environment that validate the SLAM approach. The process of observing a visual landmark is simulated in the following way: first, the relative measurement obtained by the robot is corrupted with Gaussian noise, using a noise model for a standard stereo camera. Second, the visual description of the landmark is altered by noise, simulating the changes in the descriptor which may occur when the robot observes the same landmark under different scales and viewpoints. In addition, the noise in the odometry of the robots also takes values obtained from real robots. We propose an approach to manage data associations in the context of visual features. Different experiments have been performed, with variations in the path followed by the robots and the parameters in the particle filter. Finally, the results obtained in simulation demonstrate that the approach is suitable for small robot teams.