Exploring Underwater Environments with Curiosity

Girdhar, Yogesh; Dudek, Gregory

doi:10.1109/crv.2014.22

Cited by 15 publications

(9 citation statements)

References 27 publications

(28 reference statements)

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“…In the underwater environment, it has been mainly used for semantic image segmentation [52,53], which can also be applied to exploration [54]. To the best of the authors' knowledge, semantic mapping has not yet been applied to point clouds obtained underwater with a laser scanner for IMR tasks, and thus, this paper goes beyond the state of the art.…”

Section: Semantic Mappingmentioning

confidence: 99%

Semantic Mapping for Autonomous Subsea Intervention

Vallicrosa

Himri

Ridao

et al. 2021

Sensors

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This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

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Section: Semantic Mappingmentioning

confidence: 99%

Semantic Mapping for Autonomous Subsea Intervention

Vallicrosa

Himri

Ridao

et al. 2021

Sensors

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“…as the curiosity module, comprises of a pan-tilt camera unit controlled by an interestingness detection algorithm based on ROST [40], a realtime spatiotemporal topic modeling technique. ROST has been previously used for developing curious exploration robots that detect and gather data about interesting phenomena in the world [41,42]. The general idea behind the use of ROST for modeling curiosity in a robotic platform is the following.…”

Section: Curiosity Modulementioning

confidence: 99%

Control, localization and human interaction with an autonomous lighter-than-air performer

St-Onge

Brèches

Sharf

et al. 2017

Robotics and Autonomous Systems

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Due to the recent technological progress, Human-Robot Interaction (HRI) has become a major field of research in both engineering and artistic realms, particularly so in the last decade. The mainstream interests are, however, extremely diverse: challenges are continuously shifting, the evolution of robot skills, as well as the advances in methods for understanding their environment radically impact the design and implementation of research prototypes. When directly deployed in a public installation or artistic performances, robots help foster the next level of understanding in HRI. To this effect, this paper presents a successful interdisciplinary art-science-technology project, the Aerostabiles, leading to a new way of conducting HRI research. The project consists of developing a mechatronic, intelligent platform embodied in multiple geometric blimps-cubes-that hover and move in the air. The artistic context of this project required a number of advances in engineering on the aspects of localization and control systems, flight dynamics, as well as interaction strategies, and their evolution through periods of collective activities called "research-creation residencies". These events involve artists, engineers, and performers working in close collaboration, sometimes, over several weeks at a time. They generate fruitful exchanges between all researchers, but most of all, they present a unique and creative way to direct and focus the robotics development. This paper represents an overview of the technical contributions from a range of expertise through the artistic drive of the Aerostabiles project.

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“…Once these are generated, algorithmic analysis can be performed with low latency using relatively limited computational resources. Examples of robotic applications that could be facilitated using image representation include information-gain-aware path planning for representative surveys [1], [2], target-aware seek and sample missions [3], curiosity-driven exploratory surveys [4], and real-time habitat inference [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Metadata in Representation Learning With Georeferenced Seafloor Imagery

Yamada

Massot-Campos

Prügel-Bennett

et al. 2021

IEEE Robot. Autom. Lett.

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Camera equipped Autonomous Underwater Vehicles (AUVs) are now routinely used in seafloor surveys. Obtaining effective representations from the images they collect can enable perception-aware robotic exploration such as information-gainguided path planning and target-driven visual navigation. This paper develops a novel self-supervised representation learning method for seafloor images collected by AUVs. The method allows deep-learning convolutional autoencoders to leverage multiple sources of metadata to regularise their learning, prioritising features observed in images that can be correlated with patterns in their metadata. The impact of the proposed regularisation is examined on a dataset consisting of more than 30k colour seafloor images gathered by an AUV off the coast of Tasmania. The metadata used to regularise learning in this dataset consists of the horizontal location and depth of the observed seafloor. The results show that including metadata in self-supervised representation learning can increase image classification accuracy by up to 15% and never degrades learning performance. We show how effective representation learning can be applied to achieve class balanced representative image identification for summarised understanding of imbalanced class distributions in an unsupervised way.

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Exploring Underwater Environments with Curiosity

Cited by 15 publications

References 27 publications

Semantic Mapping for Autonomous Subsea Intervention

Semantic Mapping for Autonomous Subsea Intervention

Control, localization and human interaction with an autonomous lighter-than-air performer

Leveraging Metadata in Representation Learning With Georeferenced Seafloor Imagery

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