Albert Palomer

I-AUV docking and intervention in a subsea panel

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities: the maintenance of permanent underwater structures as well as the recovery of benthic stations or black-boxes are a few of them. These tasks are addressed nowadays using manned submersibles or work-class remotely operated vehicles (ROVs), equipped with teleoperated arms under human supervision. In the context of the TRITON Spanish funded project, a subsea panel docking and an intervention procedure are proposed. The light-weight intervention AUV (I-AUV) Girona 500 is used to autonomously dock into a subsea panel using a funnel-based docking method for passive accommodation. Once docked, an autonomous fixed-based manipulation system, which uses feedback from a digital camera, is used to turn a valve and plug/unplug a connector. The paper presents the techniques used for the autonomous docking and manipulation as well as how the adapted subsea panel has been designed to facilitate such operations.

Active Range-Only beacon localization for AUV homing

This paper presents a solution to the 3D Range-Only beacon localization problem using a Sum of Gaussians (SOG) filter together with an Active Localization method, which is based on the minimization of the beacon position uncertainty, in order to ensure the problem observability and a fast convergence. The method is applied to autonomously locate a subsea panel and home to it in order to establish visual contact to later launch a visual servoing based docking task. The method is demonstrated through field experiments in a harbor environment with an Autonomous Underwater Vehicle (AUV) including Ultra-Short Baseline (USBL) ground truth information.

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.

Multibeam 3D underwater SLAM with probabilistic registration

This paper describes a pose-based underwater 3D Simultaneous Localization and Mapping (SLAM) using a multibeam echosounder to produce high consistency underwater maps. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches (i.e., point clouds). An Iterative Closest Point (ICP) with a probabilistic implementation is then used to register the point clouds, taking into account their uncertainties. The registration process is divided in two steps: (1) point-to-point association for coarse registration and (2) point-to-plane association for fine registration. The point clouds of the surfaces to be registered are sub-sampled in order to decrease both the computation time and also the potential of falling into local minima during the registration. In addition, a heuristic is used to decrease the complexity of the association step of the ICP from O(n2) to O(n). The performance of the SLAM framework is tested using two real world datasets: First, a 2.5D bathymetric dataset obtained with the usual down-looking multibeam sonar configuration, and second, a full 3D underwater dataset acquired with a multibeam sonar mounted on a pan and tilt unit.

Uncertainty-driven survey path planning for bathymetric mapping

We present a novel survey path planning technique which minimizes the robots position uncertainty along the planned path while taking into account area coverage performance. The proposed technique especially targets bathymetric mapping applications and respects application constraints such as the desire to survey in parallel tracks and to avoid turns in the target area to maximize sonar measurements quality. While accounting for uncertainty in the survey planning process can lead to more accurate data products, existing survey planning tools typically ignore it. Our method bridges this gap using the saliency on an a priori map to predict how the terrain will affect the robots belief at every point on the target area. Based on this magnitude, we provide an algorithm that computes the order in which to trace parallel tracks to cover the target area minimizing the overall uncertainty along the path. A particle filter keeps track of the robots position uncertainty during the planning process and, in order to find useful loop-closures for mapping, crossing tracks that visit salient locations are added when the uncertainty surpasses a user-provided threshold. We test our method on real-world datasets collected off the coasts of Spain, Greece and Australia. We evaluate the expected robots position uncertainty along the planned paths and assess their associated mapping performance using a bathymetric mapping algorithm. Results show that our method offers benefits over a standard lawnmower-type path both in terms of position uncertainty and map quality.

Underwater 3D Laser Scanners: The Deformation of the Plane

Development of underwater 3D perception is necessary for autonomous manipulation and mapping. Using a mirror-galvanometer system to steer a laser plane and using triangulation, it is possible to produce full 3D perception without the need of moving the sensor. If the sensor does not meet certain hardware requirements, the laser plane is distorted when it passes through the different media (air–viewport–water). However, the deformation of this plane has not been studied. In this work a ray-tracing model is presented to study the deformation of the laser plane. To validate it, two types of datasets have been used, one synthetically generated using the model presented below, and another one using real data gathered underwater with an actual laser scanner. For both datasets an elliptic cone is fitted on the data and compared to a plane fit (the surface commonly used for triangulation). In the two experiments, the elliptic cone proved to be a better fit than the plane.

Realtime AUV Terrain Based Navigation with Octomap in a Natural Environment

This paper addresses the problems of Terrain Based Navigation (TBN) and Occupancy Grid Mapping for an Autonomous Underwater Vehicle (AUV). The two problems are solved using the same tools to make feasible in future works to implement a Simultaneous Localization and Mapping (SLAM). Realtime Occupancy Grid Mapping on the real vehicle Girona500 AUV is achieved by means of the Octomap library. The resulting map is later used for TBN with the parallelized execution of a Particle Filter making also use of the Octomap library to compare multibeam sonar ranges against the known map. The Occupancy Grid Mapping and the Particle filter are implemented as individual nodes in the vehicle’s software architecture in ROS. Tests were carried out in a dataset of a natural environment near the coast. Several parameters involving the Particle Filter (number of particles, number of beams, uncertainty of measurements) are studied. Finally, the results are compared with the dead reckoning obtained by the AUV and the USBL positions obtained from a surface boat.

3D Laser Scanner for Underwater Manipulation

Nowadays, research in autonomous underwater manipulation has demonstrated simple applications like picking an object from the sea floor, turning a valve or plugging and unplugging a connector. These are fairly simple tasks compared with those already demonstrated by the mobile robotics community, which include, among others, safe arm motion within areas populated with a priori unknown obstacles or the recognition and location of objects based on their 3D model to grasp them. Kinect-like 3D sensors have contributed significantly to the advance of mobile manipulation providing 3D sensing capabilities in real-time at low cost. Unfortunately, the underwater robotics community is lacking a 3D sensor with similar capabilities to provide rich 3D information of the work space. In this paper, we present a new underwater 3D laser scanner and demonstrate its capabilities for underwater manipulation. In order to use this sensor in conjunction with manipulators, a calibration method to find the relative position between the manipulator and the 3D laser scanner is presented. Then, two different advanced underwater manipulation tasks beyond the state of the art are demonstrated using two different manipulation systems. First, an eight Degrees of Freedom (DoF) fixed-base manipulator system is used to demonstrate arm motion within a work space populated with a priori unknown fixed obstacles. Next, an eight DoF free floating Underwater Vehicle-Manipulator System (UVMS) is used to autonomously grasp an object from the bottom of a water tank.

A Comparison of G2o Graph SLAM and EKF Pose Based SLAM with Bathymetry Grids

Abstract This paper address the Simultaneous Localization and Mapping (SLAM) problem of an AUV using bathymetric maps. The algorithm compounds swath profiles of the seafloor with DVL navigation(dead-reckoning) to build surface patches (3D point clouds). An initial guess of the location of these point clouds is known a priori by means of the dead-reckoning solution. Whenever there is a significant overlap of two or more point clouds, the corresponding surface patches are registered among themselves using a probabilistic ICP algorithm. The outcome of the registration procedure is a set of constrains defining the relative position of the overlapping surface patches. Next, these constrains are used to optimize a pose graph using the G2o optimizer. The results are compared against our prior EKF-pose-based SLAM solution. Our results suggest that a better performance is achieved using EKF global optimization with respect to the G2o graph-SLAM solution.