In image processing phase correlation has been shown to outperform feature matching in several contexts. In this paper, a novel volume registration technique is proposed for solving the simultaneous localization and mapping (SLAM) problem. Unlike existing methods which rely on iterative feature matching, the proposed method utilises 3D phase correlation. This method provides high noise robustne...

Multi-robot systems play an important role in many robotic applications. A prerequisite for a team of robots is the capability of building and maintaining updated maps of the environment. The simultaneous estimation of the trajectory and the map of the environment (known as SLAM) requires many computational resources. Moreover, SLAM is generally performed in environments that do not vary over t...

This paper establishes an autonomous monitoring framework to augment a human’s ability to detect changes in lakeshore environments. This is a large spatial and temporal scale study, which analyzes data from eight different surveys of a lakeshore collected over 11 months with an autonomous surface vehicle. Despite the variation in appearance across surveys, our framework provides a human with al...

Figure 1: Framework overview. An image sequence is rendered and passed to the evaluated VSLAM system. A log of the systems operation is created and processed by the evaluation system. From the known scene structure and trajectory, the log is used to compute a sparse ground truth feature map, as well as ground truth (or rather “ideal”) measurements. Finally, plot data is generated comparing esti...

We address the problem of simultaneous localization and mapping (SLAM) by combining visual loop-closure detection with metrical information given by a robot odometry. The proposed algorithm extends a purely appearance-based loopclosure detection method based on bags of visual words [1] which is able to detect when the robot has returned back to a previously visited place. An efficient optimizat...

A FastSLAM approach to the SLAM problem is considered in this paper. An improvement to the classical FastSLAM algorithm has been obtained by replacing the Extended Kalman Filters used in the prediction step and in the feature update with Unscented Kalman Filters and by introducing an adaptive selective resampling. The simulations confirm the effectiveness of the proposed modifications.

Most algorithms for simultaneous localization and mapping (slam) do not incorporate prior knowledge of structural or geometrical characteristics of the environment. In some cases, such information is readily available and making some assumptions is reasonable. For example, it is often safe to assume that many walls in an indoor environment are rectilinear. In this paper, we develop a slam algor...

The increasing use ofAutonomous Underwater Vehicles (AUV) in industrial or scientific applications makes the vehicle localization one of the challenging questions to consider for the mission success. GraphSLAM has emerged as a promising approach in land vehicles, however, due to the complexity of the aquatic media, these systems have been rarely applied in underwater vehicles. The few existing ...

This paper describes a real-time implementation of feature-based concurrent mapping and localization (CML) running on a mobile robot in a dynamic indoor environment. Novel characteristics of this work include: (1) a hierarchical representation of uncertain geometric relationships that extends the SPMap framework, (2) use of robust statistics to perform extraction of line segments from laser dat...