InfoOdometry | Information-Theoretic Odometry Learning
kandi X-RAY | InfoOdometry Summary
kandi X-RAY | InfoOdometry Summary
InfoOdometry is a Python library. InfoOdometry has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
This is the official PyTorch implementation for [Information-Theoretic Odometry Learning], IJCV 2022.
This is the official PyTorch implementation for [Information-Theoretic Odometry Learning], IJCV 2022.
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InfoOdometry has a low active ecosystem.
It has 13 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
InfoOdometry has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of InfoOdometry is current.
Quality
InfoOdometry has no bugs reported.
Security
InfoOdometry has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
InfoOdometry does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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InfoOdometry releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of InfoOdometry
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of InfoOdometry
InfoOdometry Key Features
No Key Features are available at this moment for InfoOdometry.
InfoOdometry Examples and Code Snippets
No Code Snippets are available at this moment for InfoOdometry.
Community Discussions
No Community Discussions are available at this moment for InfoOdometry.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install InfoOdometry
Install necessary python packages. Setup necessary folders and files. Setup kitti dataset for visual-inertial odometry.
Install necessary python packages pip install -r requirements.txt
Setup cuda environment set CUDA_HOME e.g. CUDA_HOME="/path/to/cuda-10.0 set LD_LIBRARY_PATH e.g. /path/to/cuda/cuda-10.0/lib64
Install correlation_package if only use --use_img_prefeat Step 3 can be skipped But need to download pre-saved features generated by python scripts/prepare_flownet_features set PYTHONPATH e.g. PYTHONPATH="$PYTHONPATH:/path/to/correlation_package" e.g. PYTHONPATH="$PYTHONPATH:/path/to/channelnorm_package" e.g. PYTHONPATH="$PYTHONPATH:/path/to/resample2d_package" bash flownet_install.sh
Setup necessary folders and files mkdir -p ckp/tmp/src/ mkdir -p ckp/pretrained_flownet/ download FlowNet2-C_checkpoint.pth.tar into this folder mkdir tb_dir mkdir eval mkdir data cd data ln -s ~/data/euroc euroc python scripts/preprocessing.py python scripts/prepare_flownet_features.py
Setup kitti dataset for visual-inertial odometry download odometry dataset from kitti odometry leaderboard: data/kitti/odometry/dataset download sync datasets for sequences 00,01,02,04,05,06,07,08,09,10 move the folders image_02 and oxts into (e.g. data/kitti/odometry/dataset/sync/00/) contains folders image_02 and oxts download unsync datasets for sequences 00,01,02,04,05,06,07,08,09,10 move oxts into (e.g. data/kitti/odometry/dataset/raw_oxts/00/) contains folder data and dataformat.txt and timestamps.txt python scripts/match_kitti_imu.py python scripts/prepare_flownet_features --dataset kitti
Install necessary python packages pip install -r requirements.txt
Setup cuda environment set CUDA_HOME e.g. CUDA_HOME="/path/to/cuda-10.0 set LD_LIBRARY_PATH e.g. /path/to/cuda/cuda-10.0/lib64
Install correlation_package if only use --use_img_prefeat Step 3 can be skipped But need to download pre-saved features generated by python scripts/prepare_flownet_features set PYTHONPATH e.g. PYTHONPATH="$PYTHONPATH:/path/to/correlation_package" e.g. PYTHONPATH="$PYTHONPATH:/path/to/channelnorm_package" e.g. PYTHONPATH="$PYTHONPATH:/path/to/resample2d_package" bash flownet_install.sh
Setup necessary folders and files mkdir -p ckp/tmp/src/ mkdir -p ckp/pretrained_flownet/ download FlowNet2-C_checkpoint.pth.tar into this folder mkdir tb_dir mkdir eval mkdir data cd data ln -s ~/data/euroc euroc python scripts/preprocessing.py python scripts/prepare_flownet_features.py
Setup kitti dataset for visual-inertial odometry download odometry dataset from kitti odometry leaderboard: data/kitti/odometry/dataset download sync datasets for sequences 00,01,02,04,05,06,07,08,09,10 move the folders image_02 and oxts into (e.g. data/kitti/odometry/dataset/sync/00/) contains folders image_02 and oxts download unsync datasets for sequences 00,01,02,04,05,06,07,08,09,10 move oxts into (e.g. data/kitti/odometry/dataset/raw_oxts/00/) contains folder data and dataformat.txt and timestamps.txt python scripts/match_kitti_imu.py python scripts/prepare_flownet_features --dataset kitti
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For any new features, suggestions and bugs create an issue on GitHub.
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