Weizhen Zhou

TL;DR: The contribution of this paper is in the proposal of a hybrid technique to mitigate sample impoverishment such that the number of particles required and hence the computation complexities are reduced.

TL;DR: A novel stereo-based algorithm is developed that serves as a tool to examine the viability of stereo vision solutions to the simultaneous localisation and mapping (SLAM) for large indoor environments and it is shown that in a larger office environment, the proposed algorithm generates location estimates which are topologically correct, but statistically inconsistent.

TL;DR: This paper presents a practical approach to solve the simultaneous localization and mapping (SLAM) problem for autonomous mobile platforms by using natural visual landmarks obtained from an stereoscopic camera, an attempt to depart from traditional sensors in order to gain the many benefits of nature-inspired information-rich 3D vision sensors.

TL;DR: A novel vision-based sensory package and an information-efficient simultaneous localization and mapping (SLAM) algorithm that is capable of handling large datasets collected at realistic sampling rates are presented.

TL;DR: An algorithm to reduce computational cost for real-time systems by giving robots the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process is proposed.