Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Robot and Sensor Networks for First Responders
IEEE Pervasive Computing
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Multi-robot Search for a Moving Target
International Journal of Robotics Research
Robotic Mapping Using Measurement Likelihood Filtering
International Journal of Robotics Research
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
EcoIMU: A Dual Triaxial-Accelerometer Inertial Measurement Unit for Wearable Applications
BSN '10 Proceedings of the 2010 International Conference on Body Sensor Networks
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios
IEEE Transactions on Signal Processing - Part I
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
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We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes' locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node's path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.