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Efficient monocular visual odometry for ground vehicles on ARM processors
| Date | Stars |
|---|---|
| 2026-07-30 | 256 |
| 2026-08-24 | 257 |
| 2026-11-04 | 257 |
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# ARM-VO 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. ## Results on KITTI dataset | Sequence 05 | Sequence 07 | Sequence 10 | |:---:|:---:|:---:| | <img src="docs/assets/Sequence5.png" width="100%"> | <img src="docs/assets/Sequence7.png" width="100%"> | <img src="docs/assets/Sequence10.png" width="100%"> | ## What's new in v2? - Results are deterministic - Scale estimation is more accurate - Camera pitch angle is no longer required (providing camera height is enough) - RGB and BGR inputs are supported - Distorted images are supported - Keypoint tracking is faster by re-using KLT pyramids - Motion estimation is more robust in dynamic environments - The API and the implementation are much cleaner - Added Python bindings - Enabled x86 to simplify development - Added CI - Removed ROS node examples (will be added in a near future) ## Build and install ARM-VO requires C++17, CMake 3.20+, OpenCV 4.5.0+, and either ncnn or TensorRT 8.6.x (TensorRT is preferred if available). Note that you don't need to install all of the mentioned dependencies. ARM-VO will first check your system to find most of them. If not found, it'll start to fetch and build them (needs network obviously).So, all you need to do is to install build dependencies: ```bash sudo apt install build-essential git cmake pkg-config libprotobuf-dev protobuf-compiler ``` Then build and install ARM-VO: ```bash 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 .. ``` #### Build Options | Option | Default | Purpose | Extra dependencies | |---|---|---|---| | `INFERENCE_BACKEND` | `AUTO` | Explicit selection for inference backend; You can set it to `NCNN` or `TENSORRT` or `AUTO` (prefers TensorRT over Ncnn when available) | `TENSORRT` requires an installed TensorRT 8.6.x with `trtexec` and the CUDA toolkit | | `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 | `python3 -m pip install pybind11 numpy` | | `BUILD_TESTS` | `OFF` | Unit tests | Catch2 v2.13.10: automatically detected if available, otherwise fetched and built by CMake. | #### Uninstall Keep the build directory after installation: it contains the uninstall script and separate manifests for ARM-VO and dependencies installed by this build. From the build directory, uninstall ARM-VO: ```bash sudo make uninstall sudo ldconfig ``` To also remove dependencies that ARM-VO built and installed: ```bash sudo make uninstall-with-dependencies sudo ldconfig ``` ## Run on KITTI dataset Download the [color odometry images](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_odometry_color.zip). From the ARM-VO repository root, run with the matching [configuration](cli/KITTI_configs/rectified): ```bash ./build/cli/run_armvo --image_folder=/path/to/sequences/00/image_2 --config=cli/KITTI_configs/rectified/Seq00-02.yaml ``` To evaluate accuracy, download the [ground-truth poses](https://s3.eu-central-1.amazonaws.com/avg-kitti/data_odometry_poses.zip) and add `--gt_poses=path/to/poses/00.txt` to the above command. ## How to use ARM-VO in your project? Check the [C++ example](examples/cpp) or [Python example](examples/python) to see how to use ARM-VO in your project. ## Limitations - 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. ## Notes - 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-
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cee95919b473b44f, topic:opencv
matched fp:cee95919b473b44f, topic:ros