Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Distributed Multi-Robot Exploration and Mapping
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Approximate distributed Kalman filtering in sensor networks with quantifiable performance
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Springer Handbook of Robotics
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Fast and accurate map merging for multi-robot systems
Autonomous Robots
Robotic Mapping and Exploration
Robotic Mapping and Exploration
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Robot-to-Robot Relative Pose Estimation From Range Measurements
IEEE Transactions on Robotics
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In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.