Angelos Mallios

Girona 500 AUV: From Survey to Intervention

This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.

Robotic Tools for Deep Water Archaeology : Surveying an Ancient Shipwreck with an Autonomous Underwater Vehicle

The goals of this article are twofold. First, we detail the operations and discuss the results of the 2005 Chios ancient shipwreck survey. This survey was conducted by an international team of engineers, archaeologists, and natural scientists off the Greek island of Chios in the northeastern Aegean Sea using an autonomous underwater vehicle (AUV) built specifically for high‐resolution site inspection and characterization. Second, using the survey operations as context, we identify the specific challenges of adapting AUV technology for deep water archaeology and describe how our team addressed these challenges during the Chios expedition. After identifying the state of the art in robotic tools for deep water archaeology, we discuss opportunities in which new developments and research (e.g., AUV platforms, underwater imaging, remote sensing, and navigation techniques) will improve the rapid assessment of deep water archaeological sites. It is our hope that by reporting on the Chios field expedition we can both describe the opportunities that AUVs bring to fine‐resolution seafloor site surveys and elucidate future opportunities for collaborations between roboticists and ocean scientists. (Less)

EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

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.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method utilizes two Extended Kalman Filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment 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. The algorithm has been tested on an AUV guided along a 600m path within a marina environment, showing the viability of the proposed approach.

Navigating and mapping with the SPARUS AUV in a natural and unstructured underwater environment

In spite of the recent advances in unmanned underwater vehicles (UUV) navigation techniques, robustly solving their localization in unstructured and unconstrained areas is still a challenging problem. In this paper, we propose a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in the unknown and 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 raw data from the sensors are processed and fused in-line with an augmented state extended Kalman filter (EKF), that estimates and keeps the scans poses. The proposed SLAM method has been tested with a real world dataset acquired from the Sparus AUV, guided in a natural underwater environment.

Probabilistic sonar scan matching SLAM for underwater environment

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 and is compared against previous work from the authors, showing the viability of the proposed approach.

Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results.

Occupancy Grid Mapping in an Underwater Structured Environment

Abstract This paper presents practical results about the occupancy grid mapping of an underwater man-made environment using a sensor suite commonly available in nowadays Autonomous Underwater Vehicles (AUVs). The proposed algorithms are tested to be incorporated as part of the design of a new motion control system to integrate reactive obstacle avoidance with local path planning techniques to provide safe real-time guidance capabilities. The paper focus on the use of a sonar scan matching improved dead-reckoning navigation (Doppler Velocity Log (DVL) and Motion Reference Unit (MRU) based) together with an standard occupancy grid mapping algorithm. A conventional inverse sensor model for a sonar profiler is used and compared against a new inverse sensor model proposed to take advantage of the use of widely available imaging sonars. The system is validated experimentally on a dataset gathered with an AUV guided along a 600 m path within a marina environment.

Delayed state information filter for USBL-Aided AUV navigation

This paper presents a navigation system for an Autonomous Underwater Vehicle (AUV) which merges standard dead reckoning navigation data with absolute position fixes from an Ultra-Short Base Line (USBL) system. Traditionally, the USBL transceiver is located on the surface, which makes necessary to feed the position fixes back to the AUV by means of an acoustic modem. An Information filter, which maintains a bounded circular buffer of past vehicle poses, is in charge of the sensor data fusion while dealing with the delays induced by the acoustic communication. The method is validated using a data set gathered for a dam inspection task.

Probabilistic surface matching for bathymetry based SLAM

This paper describes a probabilistic surface matching method for pose-based bathymetry SLAM using a multibeam sonar profiler. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches. Then, a probabilistic implementation of the ICP is used to deal with the uncertainty of the robot pose as well as the measured points in a two-stage process including point-to-point and point-to-plane metrics. A novel surface adaptation using octrees is proposed to have ICP-derived methods working in feature-poor or highly unstructured areas typical of bathymetric scenarios. Moreover, a heuristic based on the uncertainties of the surface points is used to improve the basic algorithm, decreasing the ICP complexity to O(n). The performance of the method is demonstrated with real data from a bathymetric survey.