Slam based on quantities invariant of the robot's configuration

This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem in the stochastic map framework for a mobile robot navigating in an indoor environment. The approach is based on the concept of the relative map. The idea consists in introducing a map state, which only contains quantities invariant under translation and rotation. In this way the landmark estimation is decoupled from the robot motion and therefore the estimation does not rely on the unmodeled error sources of the robot motion. A new landmark is introduced by considering the intersection point between two lines. Only landmarks whose position error is small are considered. In this way the intersection point is the natural extension of the corner feature. The relative state estimated through a Kalman filter contains the distances among the intersection points observed at the same time. Real experiments carried out with a mobile robot equipped with a 360o laser range finder show the performance of the approach

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

D-SLAM: A decoupled solution to simultaneous localization and mapping

The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM with a range and bearing sensor in an environment populated with point features can be decoupled into solving a nonlinear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. This is achieved by transforming the measurement vector into two parts: one containing information relating features in the map and another with information relating the map and robot. It is shown that the new formulation results in an exactly sparse information matrix for mapping when it is solved using an Extended Information Filter (EIF).Thus a significant saving in the computational effort can be achieved for large-scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among features in the map are still kept and it is demonstrated that the uncertainty of the feature estimates monotonically decreases. The algorithm is illustrated and evaluated through computer simulations and experiments. © 2007 SAGE Publications

Multi-resolution SLAM for Real World Navigation

In this paper a hierarchical multi-resolution approach allowing for high precision and distinctiveness is presented. The method combines topological and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. For the topological framework the fingerprint sequence approach is used. During the construction of the topological map, a communication between the two paradigms is established. The fingerprint used for topological navigation enables also the re-initialization of the metric localization. The experimentation section will validate the multi-resolution-representation maps approach and presents different steps of the method

Implementation issues and experimental evaluation of D-SLAM

© Springer-Verlag Berlin Heidelberg 2006. D-SLAM algorithm first described in [1.] allows SLAM to be decoupled into solving a non-linear static estimation problem for mapping and a three-dimensional 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) computational cost where N is the total number of features (landmarks). The theoretical foundations of D-SLAM together with implementation issues including data association, state recovery, and computational complexity are addressed in detail. Evaluation of the D-SLAM algorithm is provided using both real experimental data and simulations