Simultaneous Localization and Mapping (SLAM) refers to the problem of using various sensors like laser scanner, RGB cameras, RGB-D cameras, etc, to estimate the position of the robot, and concurrently construct the 2D/3D map of the environment. The SLAM community has made great progress in the past few decades. So far the 2D SLAM problem with range finders is considered as solved, while the rea...

This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are ...

This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the t...

This paper presents a robust multi-loop airborne SLAM structure which augments wind information into the state of 6DoF Simultaneous Localisation and Mapping (SLAM). The relative air velocity observation from an air data system can be used to estimate the error of the vehicle state. However due to a priori unknown wind information, it cannot directly be used for that purpose. This can be tackled...

Simultaneous Localization and Mapping (SLAM) refers to the problem of mapping an unknown environment that the robot is operating in and localizing itself in the unknown environment at the same time. Out of the various methods of performing SLAM, using a single monocular camera as the sole sensory input is highly preferred due to its simplicity and low power consumption. Range sensors such as la...

Simultaneous Localization And Mapping(SLAM) is useful when a robot explores an unknown environment. Since this ability is required in many real-world applications, SLAM is an important topic in robotics. Visual SLAM is solving the the SLAM problem by visual information such as images or video. Visual SLAM is important for robot navigation, but it can also be applied to mobile devices, such as s...

This tutorial provides an introduction to the Simultaneous Localisation and Mapping (SLAM) method and the extensive research on SLAM that has been undertaken. Part I of this tutorial described the essential SLAM problem. Part II of this tutorial (this paper) is concerned with recent advances in computational methods and in new formulations of the SLAM problem for large scale and complex

This tutorial provides an introduction to the Simultaneous Localisation and Mapping (SLAM) method and the extensive research on SLAM that has been undertaken. Part I of this tutorial described the essential SLAM problem. Part II of this tutorial (this paper) is concerned with recent advances in computational methods and in new formulations of the SLAM problem for large scale and complex

This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own location. The past decade has seen rapid and exciting progress in solving the SLAM problem toget...