mobi1019 / deep_underwater_localization Goto Github PK

Source Code for the paper DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization, accepted to IROS 2020. [Paper]

We propose a real-time deep-learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from single image. Due to the pro-found difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP.

If you find DeepURL useful in your research, please consider citing:

@misc{joshi2020deepurl,
    title={DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization},
    author={Bharat Joshi and Md Modasshir and Travis Manderson and Hunter Damron and Marios Xanthidis and Alberto Quattrini Li and Ioannis Rekleitis and Gregory Dudek},
    year={2020},
    archivePrefix={arXiv}
}

Packages

• Python 3, Tensorflow >= 1.8.0, Numpy, tqdm, opencv-python

Tested on

• Ubuntu 18.04
• Tensorflow 1.15.0
• python 3.7.6
• Cuda Toolkit 10.0

There are some images from Pool Dataset under ./data/demo_data. You can run the demo on single image by

1. Download the pretrained DeepURL checkpoint,deepurl_checkpoint.zip, from [GitHub Release] and extract the checkpoint.

2. python test_single_image.py --input_image data/demo_data/1537054379109932699.jpeg --checkpoint_dir path_to_extracted_checkpoint

Sample Result:

Note: DeepURL only supports one object class until now

1. Download the pretrained darknet Tensorflow checkpoint,darknet_weight_checkpoint.zip, from [GitHub Release]. Extract the darknet checkpoint and place inside ./data/darknet_weights/ directory.

2. Download the synthetic dataset - synthetic.zip obtained after image-to-image translation using CycleGAN from [AFRL DeepURL Dataset] and extract them. The training file is available as .data/deepurl/train.txt. Each line in the training file represents each image in the format like image_index image_absolute_path img_width img_height label_index 2D_bounding_box 3D_keypoint_projection. 2D_bounding_box format: x_min y_min x_max y_max top left -> (x_min,y_min) and bottom right -> (x_max, y_max). 3D_keypoint_projection contains the projections of 8 corners of Aqua (any other object you want to use) 3D object model in the image.

   For example:

   0 xxx/xxx/45162.png 800 600 0 445 64 571 234 505 151 519 243 546 227 555 209 586 191 440 119 466 105 458 61 489 44
   1 xxx/xxx/3621.png 800 600 0 194 181 560 475 400 300 356 509 305 417 207 422 166 358 620 243 602 169 442 245 422 191
   

   To train change the image_absolute_path to the directory where you downloaded and extracted the synthetic dataset.

   Please refer to this link for a detailed explanation on how to create labels for your own dataset.

3. Start the training

   python train.py

   The hyper-parameters and the corresponding annotations can be found in args.py. For future work, projections of 3D Aqua center are also appended at the end. Change nV to 9 in args.py if you want to use center of object as keypoint for training.

1. Download the pretrained DeepURL checkpoint,deepurl_checkpoint.zip, from [GitHub Release] and extract the checkpoint.

2. Download the pool dataset - pool.zip from [AFRL DeepURL Dataset] and extract them. The testing file is available as ./data/deepurl/pool_test.txt. Each line in the training file represents each image in the format like image_index image_absolute_path img_width img_height label_index 3D_keypoint_projection. 3D_keypoint_projection contains the projections of 8 corners of Aqua (any other object you want to use) 3D object model in the image.

   For example:

   0 xxx/xxx/1536966071809545499.jpeg 800 600 0 630 278 644 237 436 287 432 249 582 111 589 68 433 125 430 85 278 644
   1 xxx/xxx/1536966073192620099.jpeg 800 600 0 590 385 593 336 400 407 392 361 523 260 522 222 389 279 384 242 385 593
   

   To test on pool dataset, change the image_absolute_path to the directory where you downloaded and extracted the pool dataset.

3. Start testing

   python test_image_list.py --image_list data/deepurl/pool_test.txt --checkpoint_dir path_to_extracted_checkpoint

1. Download the pretrained DeepURL checkpoint,deepurl_checkpoint.zip, from [GitHub Release] and extract the checkpoint.
2. Download GoPro Video from [Google Drive]

3. python test_video.py --test_video path_to_downloaded_test_video --checkpoint_dir path_to_extracted_checkpoint

This code is built on YOLOv3 implementation of github user @wizyoung.

For any help, enquiries and comments, please contact me at [email protected].