Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Probabilistic self-localization for sensor networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Hybrid inference for sensor network localization using a mobile robot
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Robot-to-Robot Relative Pose Estimation From Range Measurements
IEEE Transactions on Robotics
IEEE Communications Magazine
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
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In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.