Readme

• This is an unofficial PyTorch implementation of ICRA 2017 paper DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
• Model
  • 

Usage

• Download KITTI data and our pretrained model
  • This shell KITTI/downloader.sh can be used to download the KITTI images and pretrained model
    • the shell will only keep the left camera color images (image_03 folder) and delete other data
    • the downloaded images will be placed at KITTI/images/00/, KITTI/images/01, ...
    • the images offered by KITTI is already rectified
    • the direct download link of pretrained model
  • Download the ground truth pose from KITTI Visual Odometry
    • you need to enter your email to request the pose data here
    • and place the ground truth pose at KITTI/pose_GT/
• Run 'preprocess.py' to
  • remove unused images based on the readme file in KITTI devkit
  • convert the ground truth poses from KITTI (12 floats [R|t]) into 6 floats (euler angle + translation)
  • and save the transformed ground truth pose into .npy file
• Pretrained weight of FlowNet ( CNN part ) can be downloaded here
  • note that this pretrained FlowNet model assumes that RGB value range is [-0.5, 0.5]
  • the code of CNN layers is modified from ClementPinard/FlowNetPytorch
• Specify the paths and changes hyperparameters in params.py
  • If your computational resource is limited, please be careful with the following arguments:
  • batch_size: choose batch size depends on your GPU memory
  • img_w, img_h: downsample the images to fit to the GPU memory
  • pin_mem: accelerate the data excahnge between GPU and memory, if your RAM is not large enough, please set to False
• Run main.py to train the model
  • the trained model and optimizer will be saved in models/
  • the records will be saved in records/
• Run test.py to output predicted pose
  • output to result/
  • file name will be like out_00.txt
• Run visualize.py to visualize the prediction of route
• Other files:
  • model.py: model is defined here
  • data_helper.py: customized PyTorch dataset and sampler
    • the input images is loaded batch by batch

Download trained model

Provided by alexart13.

• trained model
• optimizer

Required packages

• pytorch 0.4.0
• torchvision 0.2.1
• numpy
• pandas
• pillow
• matplotlib
• glob

Result

• Training Sequences

• Testing Sequence

Acknowledgments

• Thanks alexart13 for providing the trained model and the correct code to process ground truth rotation.

References

• paper
  • Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
  • ICRA 2017

    @inproceedings{wang2017deepvo,
    title={Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks},
    author={Wang, Sen and Clark, Ronald and Wen, Hongkai and Trigoni, Niki},
    booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
    pages={2043--2050},
    year={2017},
    organization={IEEE}
    }