Incrementally Learned Mixture Models for GNSS Localization
| Autor: | Tim Pfeifer, Peter Protzel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences 050210 logistics & transportation Current (mathematics) 010504 meteorology & atmospheric sciences Computer science 05 social sciences Bayesian inference Mixture model 01 natural sciences Sensor fusion algorithm Computer Science - Robotics Distribution (mathematics) GNSS applications 0502 economics and business FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Algorithm Parametrization Robotics (cs.RO) 0105 earth and related environmental sciences |
| Popis: | GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the sensor fusion algorithm. However, these approaches require prior knowledge about the sensor's distribution, which is often not available. We introduce a novel sensor fusion algorithm based on variational Bayesian inference, that is able to approximate the true distribution with a Gaussian mixture model and to learn its parametrization online. The proposed Incremental Variational Mixture algorithm automatically adapts the number of mixture components to the complexity of the measurement's error distribution. We compare the proposed algorithm against current state-of-the-art approaches using a collection of open access real world datasets and demonstrate its superior localization accuracy. 8 pages, 5 figures, published in proceedings of IEEE Intelligent Vehicles Symposium (IV) 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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