Ting Yuan

TL;DR: In this article, the occupancy state of each grid cell in a robot's environment is estimated by estimating the occupancy states of the grid cells in the robot's own environment map, which is a well-established approach for environment perception in robotic and automotive applications.

TL;DR: The short observation time and the estimation ambiguity between drag and thrust in the dynamic model motivate the development of a multiple interacting multiple model (MIMM) estimator with various drag coefficient initializations.

TL;DR: This paper presents a method to fuse laser and radar measurement data with the Bayesian occupancy filter, and shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of theynamic estimation.

TL;DR: In this paper, the authors analyzed the method of locating a moving target from corrupted Doppler frequency measurements obtained by single or multiply receivers located together and with corresponding cooperative or non-cooperative transmitters distributing in remote places.

TL;DR: In this article, the authors define 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 an external measurement system, and motivate an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter.