Corpus ID: 208139106

Machine Vision for Improved Human-Robot Cooperation in Adverse Underwater Conditions

  title={Machine Vision for Improved Human-Robot Cooperation in Adverse Underwater Conditions},
  author={M. Islam},
  • M. Islam
  • Published 2019
  • Computer Science
  • ArXiv
Visually-guided underwater robots are widely used in numerous autonomous exploration and surveillance applications alongside humans for cooperative task execution. However, underwater visual perception is challenging due to marine artifacts such as poor visibility, lighting variation, scattering, etc. Additionally, chromatic distortions and scarcity of salient visual features make it harder for an underwater robot to visually interpret its surroundings to effectively assist its companion diver… Expand


Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration
  • M. Islam
  • Computer Science, Engineering
  • J. Field Robotics
  • 2019
A hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules is proposed. Expand
Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration
A syntactically simple framework that can be easily adopted by divers for communicating simple instructions to underwater robots without using artificial tags such as fiducial markers or requiring memorization of a potentially complex set of language rules is presented. Expand
Enhancing Underwater Imagery Using Generative Adversarial Networks
A method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline is proposed. Expand
Fast Underwater Image Enhancement for Improved Visual Perception
The proposed conditional generative adversarial network-based model is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots and provides improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. Expand
Mixed-domain biological motion tracking for underwater human-robot interaction
  • M. Islam, Junaed Sattar
  • Engineering, Computer Science
  • 2017 IEEE International Conference on Robotics and Automation (ICRA)
  • 2017
An algorithm is presented for an autonomous underwater robot to visually detect and follow its companion human diver that allows detection of arbitrary motion directions, in addition to keeping track of a diver's position through the image sequence over time. Expand
On the performance of color tracking algorithms for underwater robots under varying lighting and visibility
  • Junaed Sattar, G. Dudek
  • Engineering, Computer Science
  • Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
  • 2006
This work focuses on quantitatively measuring the performance of three color-based tracking algorithms- color blob tracker, color histogram tracker and mean-shift tracker, in tracking objects underwater in different levels lighting and visibility. Expand
Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios
Real-world results show that a moving diver can be autonomously distinguished from stationary objects in a noisy sonar image and tracked. Expand
Underwater multi-robot convoying using visual tracking by detection
A robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments and an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections. Expand
Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light
The object detection performance on enhanced images is exploited as a brand new task-specific evaluation criterion and suggested promising solutions and new directions for visibility enhancement, color correction, and object detection on real-world underwater images are suggested. Expand
Real-world Underwater Enhancement: Challenging, Benchmark and Efficient Solutions
A large-scale Realistic Underwater Image Enhancement (RUIE) dataset, in which all degraded images are divided into multiple sub-datasets according to natural underwater image quality evaluation metric and the degree of color deviation, to exploit the object detection performance on the enhanced images as a new `task-specific' evaluation criterion for underwater image enhancement algorithms. Expand