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deep-learning-camera-pose-estimation's Introduction

Deep Learning Camera Pose Estimation

Camera Pose Estimation originally based on Kalman Filter, using Deep Learning and recurrent neural networks.

Based on the work of Jason Rambach.

Usage

The program requires two input files, one holding the imu measurements and another one holding the ground truth data.

python <network_version.py> <imu_output> <ground_truth>

Options

Option Short Version Type Meaning
--batch-size -bs Integer The Batch Size
--display-step -ds Integer How often the intermediate results should be output. Each ds-th epoch
--epochs -ep Integer How many epochs to train
--output -o String File to write the output to

Plot Results

There is a script to plot the results called plot_results.py inside the Scripts folder.

It can plot the output files by simply calling

python plot_results.py <outputfile>

Options

Option Short Version Type Meaning
--no-display -nd Flag Does not display the plot. Useful when only the output file is desired
--output -o String Output file where the plot should be stored.Will be stored in png format
--training-only -to Flag Plots only the training cost
--validation-only -vo Flag Plots only the validation cost
--model-output -mo Flag Output file where the model should be stored

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Technologies

This software is based on the TensorFlow Deep Learning Framework.

Unfortunately TensorFlows API has changed a lot here and there, sometimes documented often enough undocumented. That's why it may run with some version and won't with some others.

The version you will install depends on what OS and what distribution you use. It won't work with the latest 1.1 version but should work with something like 0.9.

If you have veresion 0.10.0 you can easily run the code when you change the line

init = tf.global_variables_initializer()

to

init= tf.initialize_all_varibles()

The latter is deprecated and no more available in later version, but the new version might not be available yet in an older version (like 0.10.0).

Literature

The original paper can be found here

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