Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots

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Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must be processed simultaneously to create a spatio-temporal model of the facility. In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations. Our approach enhances the precision of alignment in noisy environments by integrating visual information from snapshots, global positional context from masks, and noisy coordinates. We validate our method through extensive experiments using synthetic data that simulate real-world robotic operations in underwater settings. The results demonstrate very high coordinate prediction accuracy and plausible image assembly, indicating the real-world applicability of our approach. The assembled images provide clear and coherent views of the underwater environment for effective monitoring and inspection, showcasing the potential for broader use in extreme settings, further contributing to improved safety, efficiency, and cost reduction in hazardous field monitoring.

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Overview

Getting Started

Data Preparation

Training Data:

Step 1:

in generate_data.py, change

output_dir = './to_your_training_folder'
n = 10 (n for the number of images you wish to generate)

run

python generate_data.py

You should get a folder named 'to_your_training_folder' with n images.

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Step 2:

in data_process.py, change

input_images_dir = "./to_your_training_folder/"
data_dir = "./data/"

run

python data_process.py

Then you should get a folder named 'data' with n folders named pool1, pool2 .... pooln

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Test Data: You could use the similar way to get the test data, just change the folder name in generate_data.py and data_process.py.

Training

in train.py change:

data_dir = './data/'

run

python train.py

Test

in test.py change:

test_data_dir = './your_test_data_folder/'
model.load_state_dict(torch.load('./coordinate_correction_checkpoint_epoch_xx.pth'))

run

python test.py