We present an algorithm for SLAM on planar graphs. We assume that a robot moves from node to node on the graph using odometry to measure the distance between consecutive landmark observations. At each node, the robot follows a branch chosen at random, without reporting which branch it follows. A low-level process detects (with some uncertainty) the presence of landmarks, such as corners, branch...

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative l...

This paper introduces vision based loop closure detection in Simultaneous Localisation And Mapping (SLAM) using Tree of Words. The loop closure performance in a complex urban environment is examined and an additional feature is suggested for safer matching. A SLAM ground experiment in an urban area is performed using Tree of Words, a delayed state information filter and planar laser scans for r...

This thesis focuses on the development and improvement of Simultaneous Localization and Mapping algorithms. In particular, we are interested in reliable and effective techniques for unstructured large scale environments. While previous researcher focused on the analysis of class of algorithms or general effects of different algorithms (e.g. complexity, convergence or consistency), in this thesi...

In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is constrained by the behavior of the utilized ranging technology in dense cluttere...

Today’s robots are able to perform more and more complex tasks, which usually require a high degree of interaction with the environment they have to operate in. As a consequence, robotic systems should have a deep and specific knowledge of their workspaces, which goes far beyond a simple metric representation a robotic system can build up through SLAM (Simultaneous Localization and Mapping). In...

In this paper, we present a method to improve the loop closing behaviour for visual SLAM. Landmarks consist of a combination of attention regions and Harris-Laplace corners. The attention regions are detected by a visual attention system which combines image-based, bottom-up and target-related, topdown information. The ability to perform target-directed search is used to search for expected lan...

© 2012 Ohishi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Host-Virus Specificity of the Morbillivirus Receptor, SLAM, in Marine Mammals: Risk A...

RatSLAM is a system for vision-based Simultaneous Localisation and Mapping (SLAM) inspired by models of the rodent hippocampus. The system can produce stable representations of large complex environments during robot experiments in both indoor and outdoor environments. These representations are both topological and metric in nature, and can involve multiple representations of the same place as ...

This paper addresses the problem of on-line path following for a car working in unstructured outdoor environments. The partially known map of the environment is updated and expanded in real time by a Simultaneous Localization and Mapping (SLAM) algorithm. This information is used to implement global path planning. A cost graph is initially constructed followed by a search to find the near-optim...