The ability to simultaneously localize a robot and accurately map its surroundings is
considered by many to be a key prerequisite of truly autonomous robots. However, few …

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel
approach to the simultaneous localization and mapping problem that is based on fast …

The work presents a new approach to the problem of simultaneous localization and
mapping-SLAM-inspired by computational models of the hippocampus of rodents. The …

This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐
based simultaneous localization and mapping (SLAM) in outdoor environments and to …

This paper proposes an integrated Bayesian frame work for feature-based simultaneous
localization and map building (SLAM) in the general case of uncertain feature number and …

We propose an on-line algorithm for simultaneous localization and mapping of dynamic
environments. Our algorithm is capable of differentiating static and dynamic parts of the …

This article provides a comprehensive description of FastSLAM, a new family of algorithms
for the simultaneous localization and mapping problem, which specifically address hard …

This monograph describes a new family of algorithms for the simultaneous localization and
mapping (SLAM) problem in robotics, called FastSLAM. The FastSLAM-type algorithms have …

This paper presents an analysis of FastSLAM-a Rao-Blackwellised particle filter formulation
of simultaneous localisation and mapping. It shows that the algorithm degenerates with time …

The lack of publicly accessible datasets with a reliable ground truth has prevented in the
past a fair and coherent comparison of different methods proposed in the mobile robot …