SimultaneousMapping and Localization (SLAM) is a multidisciplinary problemwith ramifications within several fields. One of the key aspects for its popularity and success is the data fusion produced by SLAM techniques, providing strong and robust sensory systems even with simple devices, such as webcams in Monocular SLAM. This work studies a novel batch validation algorithm, the highest order hy...

This paper gives an introduction to the Simultaneous Localization and Mapping (SLAM) methods and recent advances in computational methods of SLAM problem for large scale & complex environment. SLAM addresses the problem of a robot navigating through an unknown environment. It is a process by which a mobile robot can build a map of its near-by environment and at the same time use this map to com...

In the last years, the Monocular SLAM problem was widely studied, to allow the simultaneous reconstruction of the environment and the localization of the observer, by using a single camera. As for other SLAM problems, a frequently used feature for the representation of the world, is the 3D point. Differently from other SLAM problems, because of the perspective model of the camera, in Monocular ...

D-SLAM algorithm first described in [1] allows SLAM to be decoupled into solving a non-linear static estimation problem for mapping and a threedimensional estimation problem for localization. This paper presents a new version of the D-SLAM algorithm that uses an absolute map instead of a relative map as presented in [1]. One of the significant advantages of D-SLAM algorithm is its O(N) computat...

Measles virus (MV) is a member of the Morbillivirus genus in the Paramyxoviridae family. Human signaling lymphocyte activation molecule (SLAM) acts as a cellular receptor for MV. SLAM is expressed on immature thymocytes, activated lymphocytes, macrophages and mature dendritic cells. This distribution of SLAM is in accord with lymphotropism and immunosuppressive nature of MV. Canine distemper an...

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or ...

This paper presents a performance analysis of omnidirectional vision based Simultaneous Localization and Mapping (SLAM). In omnidirectional vision based SLAM; robots perform vision based SLAM using only monocular omnidirectional cameras. In this paper, we mainly investigate the use of an omnidirectional camera for Extended Kalman Filter (EKF) based SLAM. To evaluate the success of omnidirection...

The signaling lymphocytic activation molecule (SLAM) family of receptors and the SLAM-associated protein (SAP) family of intracellular adaptors are expressed in immune cells. By way of their cytoplasmic domain, SLAM-related receptors physically associate with SAP-related adaptors. Evidence is accumulating that the SLAM and SAP families play crucial roles in multiple immune cell types. Moreover,...

For feature-based monocular bearing-only SLAM, how to select useful features for SLAM process is crucial. The reason is overwhelming feature number will not only seriously slow down the system but produce erroneous SLAM result due to feature mismatching. In this paper, we propose a novel method for feature selection. The method combines both bottom-up (visual saliency) and top-down (learned obj...

This paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only t...