Citation

Törő O, Bécsi T, Aradi S, Gáspár P. IFAC-PapersOnLine 2017; 50(1): 264-269.

Copyright

(Copyright © 2017, International Federation of Automatic Control, Publisher Elsevier Publishing)

DOI

10.1016/j.ifacol.2017.08.044

PMID

unavailable

Abstract

Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. A method is presented for calculating the likelihoods, suitable for particle filtering performed with moving sensors, assuming additive Gaussian measurement noise. In our approach, for calculating the posterior estimate of the object state, the measurement likelihoods are computed in the state space, instead of the measurement space, by mapping each measurement to the global coordinate system. The map consists of a nonlinear and an affine part. While the affine transformation trivially preserves the Gaussian nature, the nonlinear is well-proven to be approximated as affine too. This approach allows the particles to be drawn directly from the state space, hence the evaluation of the measurement model is not needed, which saves computational power.

Language: en

Keywords

Autonomous Vehicles; Object recognition; Obstacle detection; perception; Sensing; Sensor integration; Simulation; State estimation; Tracking