Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
The theory of evolution strategies
The theory of evolution strategies
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Distributed Multi-Robot Exploration and Mapping
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
International Journal of Robotics Research
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Fast and accurate map merging for multi-robot systems
Autonomous Robots
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Anytime merging of appearance-based maps
Autonomous Robots
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Multi-robot map merging is an essential task for cooperative robot navigation. In the realistic case, the robots do not know the initial positions of the others and this adds extra challenges to the problem. Some approaches search transformation parameters using the local maps and some approaches assume the robots will observe each other and use robot to robot observations. This work extends a previous work which is based on EKF-SLAM to the Fast-SLAM algorithm. The robots can observe each other and non-unique landmarks using visual sensors and merge maps by propagating uncertainty. Another contribution is the calibration of noise parameters with supervised data using the Evolutionary Strategies method. The developed algorithms are tested in both simulated and real robot experiments and the improvements and applicability of the developed methods are shown with the results.