ARM-VO is an efficient monocular visual odometry library for on-road vehicles. It recovers scale using a known, fixed camera height and visible road. NEON intrinsics and multithreading accelerate keypoint detection and tracking.
| Sequence 05 | Sequence 07 | Sequence 10 |
|---|---|---|
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- Deterministic results, more accurate scale estimation, and no camera pitch angle requirement.
- Faster tracking through KLT pyramid reuse and more robust motion estimation in dynamic scenes.
- Support for color (BGR or RGB) and distorted images, a much cleaner API, Python bindings, x86 support, and CI.
- ROS node examples removed (will be added in a near future)
ARM-VO requires C++17, CMake 3.20+, OpenCV, and either ncnn or TensorRT 8.6.x (TensorRT is preferred over ncnn when available).
Install build dependencies on Ubuntu:
sudo apt install build-essential git cmake pkg-config libopencv-devFor the ncnn backend:
git clone --recursive --depth 1 --branch 20241226 https://github.com/Tencent/ncnn.git
cd ncnn
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_STANDARD=17 -DNCNN_BUILD_TESTS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF -DNCNN_THREADS=ON -DNCNN_OPENMP=OFF -DNCNN_VULKAN=OFF -DNCNN_ENABLE_LTO=ON
make -j$(nproc)
sudo make install
sudo ldconfig
cd ../..Then build ARM-VO:
git clone https://github.com/zanazakaryaie/ARM-VO.git
cd ARM-VO
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)
sudo make install
sudo ldconfig
cd ..| Option | Default | Purpose | Extra dependencies |
|---|---|---|---|
BUILD_TOOLS |
ON |
Visualization, evaluation, and model conversion utilities | None |
BUILD_CLI |
ON |
Command-line tools; requires BUILD_TOOLS=ON |
None |
BUILD_PYTHON_BINDINGS |
OFF |
Python API | sudo apt install python3-dev pybind11-dev python3-numpy |
BUILD_TESTS |
OFF |
Unit tests | Catch2 v2:git clone --branch v2.13.10 --depth 1 https://github.com/catchorg/Catch2.gitcd Catch2mkdir build && cd buildcmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_STANDARD=17 -DCMAKE_INSTALL_PREFIX=/usr/local -DCATCH_BUILD_STATIC_LIBRARY=ON -DCATCH_BUILD_TESTING=OFF -DCATCH_INSTALL_DOCS=OFF -DCATCH_INSTALL_HELPERS=ON ..make -j$(nproc)sudo make installsudo ldconfig && cd ../.. |
Download the color odometry images. From the ARM-VO repository root, run with the matching configuration:
./build/cli/run_armvo --image_folder=/path/to/sequences/00/image_2 --config=cli/KITTI_configs/rectified/Seq00-02.yamlTo evaluate accuracy, download the ground-truth poses and add --gt_poses=path/to/poses/00.txt.
Check the C++ example or Python example to see how to use ARM-VO in your project.
- The camera height and road requirements make ARM-VO unsuitable for drones, handheld cameras, or off-road vehicles.
- Large rotations without translation can lose tracking.
- ARM-VO leverages a low-resolution (320x640) BisenetV2 segmentation model to 1) estimate scale, and 2) perform better in dynamic scenes. You can increase or decrease the resolution to trade-off between accuracy and FPS. Check here to read more and go through the required steps.
- If you get low FPS on single-board computers (e.g. Raspberry Pi), check your power adapter.
.
├── cli/ Command-line tools for running ARM-VO
├── cmake/ CMake scripts
├── docs/ Documentation and README assets
├── examples/ C++ and Python usage examples
├── lib/ Core ARM-VO implementation and Python bindings
├── model/ BiseNetv2 model
├── tools/ Utilities for visualization, evaluation, etc.
└── CMakeLists.txt Main CMake build file
If you build ARM-VO with -DBUILD_TESTS=ON, you can run tests from the repo root by:
ctest --test-dir build --output-on-failureARM-VO is MIT licensed. For academic use, please cite:
@article{nejad2019arm,
title={ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs},
author={Nejad, Zana Zakaryaie and Ahmadabadian, Ali Hosseininaveh},
journal={Machine Vision and Applications},
volume={30},
number={6},
pages={1061--1070},
year={2019},
publisher={Springer}
}- Increase test coverage
- Add ROS examples
- Add redundancy for scale estimation (e.g. object priors)
- Support Bazel