Gabriel Oliver

Visual Navigation for Mobile Robots: A Survey

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.

Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

Autonomous Underwater Vehicles (AUVs) operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability. Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities. To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment. Unlike previous works, the more realistic and applied case of constant thrust power navigation is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable. Conversely, the straight line path between starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent of the spatial structure of the eddy field.

A vision system for an underwater cable tracker

Abstract. Nowadays, the surveillance and inspection of underwater installations, such as power and telecommunication cables and pipelines, is carried out by operators that, being on the surface, drive a remotely operated vehicle (ROV) with cameras mounted over it. This is a tedious and high time-consuming task, easily prone to errors mainly because of loss of attention or fatigue of the human operator. Besides, the complexity of the task is increased by the lack of quality of typical seabed images, which are mainly characterised by blurring, non-uniform illumination, lack of contrast and instability in the vehicle motion. In this study, the development of a vision system guiding an autonomous underwater vehicle (AUV) able to detect and track automatically an underwater power cable laid on the seabed is the main concern. The vision system that is proposed tracks the cable with an average success rate above 90%. The system has been tested using sequences coming from a video tape obtained in several tracking sessions of various real cables with a ROV driven from the surface. These cables were installed several years ago, so that the images do not present highly contrasted cables over a sandy seabed; on the contrary, these cables are partially covered in algae or sand, and are surrounded by other algae and rocks, thus making the sequences highly realistic.

AUV navigation through turbulent ocean environments supported by onboard H-ADCP

Frequently, autonomous underwater vehicles (AUVs) must operate in turbulent ocean environments with complex spatio-temporal variability. Energetic flows, instabilities and currents can strongly perturb safety conditions and development of AUVs operations. A priori knowledge of this ocean variability would allow to adequately plan AUV missions, minimizing the possible negative effects of the environment on its operation. Unfortunately, present oceanographic technology is only capable to forecast large and slow components of the ocean variability, being fast and small scales of variability still unpredictable. This partial knowledge about future environmental conditions degrades AUV robustness, reducing its autonomy and safety. In this article, we propose a class of real-time algorithm to support AUVs navigation in strong turbulent ocean environments characterized by unpredictable small scale variability. The algorithm infers the two-dimensional structure of the current field in a limited region in front of the AUV, using the one-dimensional information obtained from an horizontal acoustic doppler current profiler (H-ADCP). Then, a planner locally optimizes the performance of the AUV in the inferred field. In this work reducing travelling time was of interest. The proposed procedure has been tested on simulated turbulent environments. Results indicate that, although sub-optimals, solutions provided by the algorithm required significantly less travelling time than straight line paths. An example where the proposed methodology increases AUV safety and autonomy is also provided

On the use of likelihood fields to perform sonar scan matching localization

Scan matching algorithms have been extensively used in the last years to perform mobile robot localization. Although these algorithms require dense and accurate sets of readings with which to work, such as the ones provided by laser range finders, different studies have shown that scan matching localization is also possible with sonar sensors. Both sonar and laser scan matching algorithms are usually based on the ideas introduced in the ICP (Iterative Closest Point) approach. In this paper a different approach to scan matching, the Likelihood Field based approach, is presented. Three scan matching algorithms based on this concept, the non filtered sNDT (sonar Normal Distributions Transform), the filtered sNDT and the LF/SoG (Likelihood Field/Sum of Gaussians), are introduced and analyzed. These algorithms are experimentally evaluated and compared to previously existing ICP-based algorithms. The obtained results suggest that the Likelihood Field based approach compares favorably with algorithms from the ICP family in terms of robustness and accuracy. The convergence speed, as well as the time requirements, are also experimentally evaluated and discussed.

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.

Intervention AUVs: The Next Challenge

While commercially available AUVs are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities. The maintenance of permanent underwater observatories, submerged oil wells, cabled sensor networks, pipes and the deployment and recovery of benthic stations are a few of them. These tasks are addressed nowadays using manned submersibles or work-class ROVs, equipped with teleoperated arms under human supervision. Although researchers have recently opened the door to future I-AUVs, a long path is still necessary to achieve autonomous underwater interventions. This paper reviews the evolution timeline in autonomous underwater intervention systems. Milestone projects in the state of the art are reviewed, highlighting their principal contributions to the field. To the best of the authors’ knowledge, only three vehicles have demonstrated some autonomous intervention capabilities so far: ALIVE, SAUVIM and GIRONA 500, being the last one the lightest one. In this paper GIRONA 500 I-AUV is presented and its software architecture discussed. Recent results in different scenarios are reported: (1) valve turning and connector plugging/unplugging while docked to a subsea panel, (2) free floating valve turning using learning by demonstration, and (3) multipurpose free-floating object recovery. The paper ends discussing the lessons learned so far.

Multipurpose autonomous underwater intervention: A systems integration perspective

Nowadays, autonomous intervention is getting more attention in the underwater robotics community. Few research projects on this matter are currently under development. In this context, and after a first successful experience in the RAUVI Spanish project (2009-2011), the authors are currently involved in the TRIDENT project (2010-2013), funded by the European Commission. To succeed in autonomous intervention, an AUV endowed with a manipulator and with a high degree of autonomy is essential. The complexity of the required robotic system is very high and the system integration process becomes critical. This paper presents the problems being solved in TRIDENT, from a systems integration perspective. As a case study, some results, achieved during the last experiments carried out in the Roses harbor (Girona) in October 2011 will be presented, to demonstrate the capabilities exhibited by the AUV for Intervention under development. The experiments were focused on the problem of autonomously searching and recovering a black-box mock-up that was previously thrown to an unknown position. This paper presents the hardware and software integration aspects that were necessary in order to address such a challenging problem.

Sonar Sensor Models and Their Application to Mobile Robot Localization

This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.

Probabilistic Sonar Scan Matching for Robust Localization

This paper presents a probabilistic framework to perform scan matching localization using standard time-of-flight ultrasonic sensors. Probabilistic models of the sensors as well as techniques to propagate the errors through the models are also presented and discussed. A method to estimate the most probable trajectory followed by the robot according to the scan matching and odometry estimations is also presented. Thanks to that, accurate robot localization can be performed without the need of geometric constraints. The experiments demonstrate the robustness of our method even in the presence of large amounts of noisy readings and odometric errors.