Variational Bayesian EM for SLAM
Paper in proceedings, 2015

Designing accurate, robust and cost-effective systems is an important aspect of the research on self-driving vehicles. Radar is a common part of many existing automotive solutions and it is robust to adverse weather and lighting conditions, as such it can play an important role in the design of a self-driving vehicle. In this paper, a radar-based simultaneous localization and mapping (SLAM) algorithm using variational Bayesian expectation maximization (VBEM) is presented. The VBEM translates the inference problem to an optimization one. It provides an efficient and powerful method to estimate the unknown data association variables as well as the map of the environment as perceived by a radar and the unknown trajectory of the vehicle.

Author

Maryam Fatemi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lars Hammarstrand

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Malin Lundgren

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015, Cancun, Mexico, 13-16 Dec. 2015

501-504 7383846

Subject Categories

Signal Processing

DOI

10.1109/CAMSAP.2015.7383846

ISBN

978-1-4799-1963-5

More information

Created

10/7/2017