CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

This paper describes a new system, dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or perfo...

Probabilistic Appearance-Based Mapping and Localization Using Visual Features

An appearance-based approach for visual mapping and localization is proposed in this paper. On the one hand, a new image similarity measure between images based on number of matchings and their associated distances is introduced. On the other hand, to optimize running times, matchings between the current image and previous visited places are determined using an index based on a set of randomize...

Visual Trajectory Based SLAM

Probabilistic feature mapping based on trajectory HMMs

This paper proposes a feature mapping algorithm based on the trajectory GMM or trajectory HMM. Although the GMM or HMM-based feature mapping algorithm works effectively, its conversion quality sometimes degrades due to the inappropriate dynamic characteristics caused by the frame-by-frame conversion. While the use of dynamic features can alleviate this problem, it also introduces an inconsisten...

Appearance-based minimalistic metric SLAM

This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots which have poor odometry and can typically only carry a single monocular camera. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the r...