Visual of Odometry Implementation in TensorFlow 2.2

This repository is an implementation of the following architectures:

• DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
• MagicVO: An End-to-End Hybrid CNN and Bi-LSTM Method for Monocular Visual Odometry

The code uses the FlowNetS pre-trained model FlowNet: Learning Optical Flow with Convolutional Networks.

Inside the main.py file is the asdas variable that serves as the configuration for the training.

• mode code execution mode, such as to train or to predict.
• datapath path where the dataset is stored.
• bsize size of batch size.
• lr learning rate value for SGD and Adagrad optimizer.
• momentum momentum value for SGD optimizer.
• train_iter number of epoch for training.
• checkpoint_path path where the checkpoint are stored.
• k default value for loss function.
• train model DeepVO or MagicVO to be trained or predicted.

Download weights

To download the weights of the models, download and place them in the checkpoints folder, where the download instructions are located.

Training

For training, the KITTI Visual Odometry dataset has been used, you can change the training sequences in the file utils/dataset.py. For example, the following variable self.sequences = ['00', '02', '08', '09'] has been used for sequences 00, 02, 08 and 09, which are the most extensive.

The structure containing the dataset must agree to the following:

<path where the dataset has been stored>\dataset

-->\poses

    --> \00.txt

    --> \01.txt

    ...

-->\sequences

    --> \00

    --> \01

    ...

• Run the main.py file with changes to the config variable for DeepVO model training

config = { 'mode': 'train', 'datapath': 'D:\EduardoTayupanta\Documents\Librerias\dataset', 'bsize': 8, 'lr': 0.001, 'momentum': 0.99, 'train_iter': 20, 'checkpoint_path': './checkpoints', 'k': 100, 'train': 'deepvo' }

• Run the main.py file with changes to the config variable for MagicVO model training

config = { 'mode': 'train', 'datapath': 'D:\EduardoTayupanta\Documents\Librerias\dataset', 'bsize': 8, 'lr': 0.001, 'momentum': 0.99, 'train_iter': 20, 'checkpoint_path': './checkpoints', 'k': 100, 'train': 'magicvo' }

Prediction