Three methods are explored which help indicate whether feature points are potentially visible or occluded in the matching phase of the keyframe-based real-time visual SLAM system. The first derives a measure of potential visibility from the angular proximity to keyframes in which they were observed and globally adjusted, and preferentially selects those with high visibility when tracking the ca...

Three-dimensional simultaneous localization and mapping is a topic of significant interest in the research community, particularly so since the introduction of cheap consumer RGB-D sensors such as the Microsoft Kinect. Current algorithms are able to create rich, visually appealing maps of indoor environments using such sensors. However, state-of-the-art systems are designed for use in static en...

The 6-DOF monocular camera case possibly represents the harder variant in the context of simultaneous localization and mapping problem. In the last years, several advances have been appeared in this area; however the application of these techniques to real world applications it’s difficult so far. Recently, the unified inverse depth parametrization has shown to be a good option this challenging...

Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly effi...

The simultaneous localization and mapping problem is approached by using an ultrasound sensor and wheel encoders. To account for the low precision inherent in ultrasound sensors, the occupancy grid notion is extended. The extension takes into consideration with which angle the sensor is pointing, to compensate for the issue that an object is not necessarily detectable from all positions due to ...

This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost al...

In visual simultaneous location and mapping (SLAM) with a single camera, the use of 3D points as a basic feature has been shown sufficient to reliably estimate the camera position and orientation. Nevertheless, the resultant maps are not clear enough for certain applications, even for a large amount of point features. We propose a novel SLAM technique that uses lines as basic features, and the ...

Recently, many authors have considered the problem of simultaneous localisation and mapping (SLAM). The paper addresses a somewhat diierent problem, that of cooperative localisation and mapping (CLAM). Basically, CLAM involves two or more robots cooperating to build a map of the environment. This cooperation is not aimed at simply increasing the speed with which the map is constructed; rather, ...

This paper presents a novel strategy for feature track initialisation in the Simultaneous Localisation and Mapping (SLAM) algorithm. Spurious observations and ambiguity in the data association during the initialisation phase of the algorithm can be resolved by initialising each observation as a new feature. When a number of observations are found to correspond to a single feature, a constraint ...

Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically require accurate detection of trees, and estimation of tree center locations in laser data. Both...