Visual localization under a wide range of operational conditions is a fundamental problem in robotics. It is a critical importance for autonomous operation in GPS-denied or complex urban environments. Our work considers robot and vehicle localization with respect to a semantic map composed of objects. The objective is to provide global (re-)localization based on recognized objects in streaming visual input. Our contribution is a sensor measurement model for set-valued observations (i.e., detections) that captures both metric and semantic information (range, bearing, and object identities) and incorporates missed and false detections and unknown data association. We proved that obtaining the likelihood of a set-valued observation is equivalent to a matrix permanent computation, which leads to an efficient polynomial-time approximation of Bayesian inference with set-valued observations. More generally, we consider continuous estimation problems with discrete observations (e.g., binary signals, object detections, context information) and develop scalable inference algorithms.
@article{Atanasov_SemanticLocalization_IJRR15,
author = {N. Atanasov and M. Zhu and K. Daniilidis and G. Pappas},
title = {Localization from Semantic Observations via the Matrix Permanent},
journal = {The International Journal of Robotics Research (IJRR)},
year = {2015},
volume = {35},
pages = {73-99},
doi = {http://www.doi.org/10.1177/0278364915596589}
}
@inproceedings{Atanasov_SemanticLocalization_RSS14,
author = {N. Atanasov and M. Zhu and K. Daniilidis and G. Pappas},
title = {Semantic Localization via the Matrix Permanent},
booktitle = {Robotics: Science and Systems (RSS)},
year = {2014},
doi = {http://www.doi.org/10.15607/RSS.2014.X.043}
}
@article{Ivanov_ContextAwareFilter_TAC19,
author = {R. Ivanov and N. Atanasov and M. Pajic and J. Weimer and G. J. Pappas and I. Lee},
title = {Continuous Estimation Using Context-Dependent Discrete Measurements},
journal = {IEEE Transactions on Automatic Control (TAC)},
year = {2019},
volume = {64},
number = {1},
pages = {235-250},
doi = {http://www.doi.org/10.1109/TAC.2018.2797839}
}
@inproceedings{Ivanov_RobustEstimation_Allerton15,
author = {R. Ivanov and N. Atanasov and M. Pajic and G. Pappas and I. Lee},
title = {Robust Estimation Using Context-Aware Filtering},
booktitle = {Allerton Conference on Communication, Control, and Computing},
year = {2015},
doi = {http://www.doi.org/10.1109/ALLERTON.2015.7447058}
}