Deep Visual Inertial Odometry
Deep learning based visual-inertial odometry project.
pros:
- Lighter CNN structure. No RNNs -> much lighter.
- Training images together with inertial data using exponential mapping.
- Rotation is coming from external attitude estimation.
- No RNN but Kalman filter: Accleration and image fusion for frame-to-frame displacement.
cons:
- no position correction: drift in position: But SLAM can correct the position drfit.
Please Cite:
Hongyun Lee, James W. Gregory, Matthew McCrink, and Alper Yilmaz. "Deep Learning for Visual-Inertial Odometry: Estimation of Monocular Camera Ego-Motion and its Uncertainty" The Ohio State University, Master Thesis, http://rave.ohiolink.edu/etdc/view?acc_num=osu156331321922759
References(current & future)
Please see paper.
Usage:
- git clone -- recursive https://github.com/ElliotHYLee/Deep_Visual_Inertial_Odometry
- Put the .m (Matlab) files under KITTI/odom/dataset/. The files are at DataGenerator folder.
- run make_trainable_data.m
- In src/Parampy, change the path for KITTI.
- At Deep_Visual_Inertial_Odometry, "python main.py"
ToDo
- upload weight.pt
- change Matlab data get to python
Prereq.s
- Matlab
- Python 3.5
pip install numpy pip install scipy pip install pandas pip install matplotlib pip install scikit-learn pip install pathlib pip install pypng pip install pillow pip install django pip install image pip install opencv-python opencv-contrib-python
detail: https://github.com/ElliotHYLee/SystemReady
Tested System
- Hardware
CPU: i9-7940x RAM: 128G, 3200Hz GPU: two Gefore 1080 ti MB: ROG STRIX x299-E Gaming
- Software
Windows10 Python3 PyTorch: v1 CUDA: v9.0 Cudnn: v7.1
Run
python main_cnn.py
Traing Results
description
Test Results
