%0 Journal Article
%T A random finite set approach for dynamic occupancy grid maps with real
%A Axel Gern
%A Dominik Nuss
%A Gunther Krehl
%A Klaus Dietmayer
%A Markus Thom
%A Michael Maile
%A Stephan Reuter
%A Ting Yuan
%J The International Journal of Robotics Research
%@ 1741-3176
%D 2018
%R 10.1177/0278364918775523
%X Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot¡¯s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results
%K environment perception
%K dynamic occupancy grid maps
%K random finite sets
%K sensor data fusion
%K autonomous driving
%K extended object tracking
%U https://journals.sagepub.com/doi/full/10.1177/0278364918775523