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Deep Visual Inertial Odometry

Deep learning based visual-inertial odometry project. <br> 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

<pre> 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 </pre>

detail: https://github.com/ElliotHYLee/SystemReady

Tested System

Hardware

<pre> CPU: i9-7940x RAM: 128G, 3200Hz GPU: two Gefore 1080 ti MB: ROG STRIX x299-E Gaming </pre>

Software

<pre> Windows10 Python3 PyTorch: v1 CUDA: v9.0 Cudnn: v7.1 </pre>

Run

python main_cnn.py

Traing Results

description

Test Results

Correction Result