In the presented scenario, an autonomous surface vehicle (ASV) equipped with a laser scanner navigates on a inland pathway surrounded and crossed by man-made structures such as bridges and locks. GPS receiver present on board experiences signal loss and multipath reflections in situation when the view of the sky is obscured by a bridge or tall buildings. In both cases, a potentially dangerous situation is provoked as the robot has no or inaccurate positioning data. A sensor data processing scheme is proposed where these gaps are smoothly filled in by positioning data generated from scan matching and registration of the laser data. This article shows preliminary results of positioning data improvement during trials in harbor-river environment.
This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas, and marine platforms. A mechanically scanned imaging sonar is used to obtain information about the location of vertical planar structures present in such environments. A robust voting algorithm has been developed to extract line features, together with their uncertainty, from the continuous sonar data flow. The obtained information is incorporated into a feature-based simultaneous localization and mapping (SLAM) algorithm running an extended Kalman filter. Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Moreover, a procedure to build and maintain a sequence of local maps and to posteriorly recover the full global map has been adapted for the application presented. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. © 2008 Wiley Periodicals, Inc. This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. 