The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits
their applicability in populated real-world environments. Furthermore, most scenarios …

Combining Simultaneous Localisation and Mapping (SLAM) estimation and dynamic scene
modelling can highly benefit robot autonomy in dynamic environments. Robot path planning …

The static world assumption is standard in most simultaneous localisation and mapping
(SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic …

We investigate a variation of the 3D registration problem, named multi-model 3D
registration. In the multi-model registration problem, we are given two point clouds picturing …

In this paper we address the problem of cooperative localization and target tracking with a
team of moving robots. We model the problem as a least squares minimization problem and …

Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for
long-term robot task completion. Failing to detect scene changes may lead to inaccurate …

The idea of SLAM (Simultaneous Localization and Mapping) being a solved problem
revolves around the static world assumption, even though autonomous systems are gaining …

Simultaneous localization and consistent mapping in dynamic environments is a
fundamental and unsolved problem in the mobile robotics community. Most of the algorithms …

Ego-pose estimation and dynamic object tracking are two critical problems for autonomous
driving systems. The solutions to these problems are generally based on their respective …

Simultaneous Localization and Mapping (SLAM) is a probabilistic approach that builds the
map of an unknown environment and estimate the position of the agent within that map at …