Timothy S. Bailey

TL;DR: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method.

TL;DR: This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained.

TL;DR: It is shown that the algorithm produces very optimistic estimates once the "true" uncertainty in vehicle heading exceeds a limit, and the manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for large-scale maps.

TL;DR: The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map-building process statistically inconsistent and it is shown that the efficiency of the SLAM can be maintained by judicious selection of landmarks, to be preserved in the map, based on their information content.

TL;DR: Among adults with overweight or obesity, once-weekly subcutaneous semaglutide compared with placebo, used as an adjunct to intensive behavioral therapy and initial low-calorie diet, resulted in significantly greater weight loss during 68 weeks.