FastTrack is an optimized visual-inertial tracking module designed to harness GPU computing power, significantly speeding up the most computationally intensive aspects of SLAM (Simultaneous Localization and Mapping). Our implementation is based on the well-established ORB-SLAM3 system.
- GPU Acceleration: We've developed several GPU kernels to offload critical components of the tracking process, enhancing overall performance.
- Stereo Feature Matching: Specialized kernels accelerate stereo feature matching for both pinhole and fisheye cameras.
- Map Point Projection: Our implementation includes efficient GPU kernels for searching map points by projection, improving the discovery of visible map points in real-time.
- Optimized ORB Feature Extraction: We integrate existing acceleration techniques for ORB feature extraction.
- Efficient Data Handling: Our design includes optimized data transfer processes between tracking components and bypasses pose optimization to enhance speed.
We have tested the library in Ubuntu 22.04 and 20.04, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results.
We use the new thread and chrono functionalities of C++14.
We have tested the library with Cuda 12.2. Download and install instructions can be found at: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/.
We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at leat 4.4.0.
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.
We use Pangolin for visualization and user interface. Download and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
Pangolin avoids to use Eigen in CUDA. To compile this project, guards in line 475 of glsl.hpp and 47 of glsl.h should be commented. Checkout this issue in ORB-SLAM3 github issues.
We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.
Required to calculate the alignment of the trajectory with the ground truth. Required Numpy module.
- (win) http://www.python.org/downloads/windows
- (deb)
sudo apt install libpython2.7-dev - (mac) preinstalled with osx
Clone the repository:
git clone [email protected]:sfu-rsl/FastTrack.git
Our system is based on ORB-SLAM3 and ORB-SLAM3 provides a script build.sh to build the Thirdparty libraries and ORB-SLAM3. Please make sure you have installed all required dependencies (see section 2). Execute:
cd FastTrack
chmod +x build.sh
sudo ./build.sh
This will create libORB_SLAM3.so at lib folder and the executables in Examples folder.
EuRoC dataset was recorded with two pinhole cameras and an inertial sensor. We provide an example script to launch EuRoC sequences in all the sensor configurations.
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Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
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Open the script "euroc_eval_examples.sh" in the Examples directory of the project. Change pathDatasetEuroc variable to point to the directory where the dataset has been uncompressed.
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Execute the following script to process all the sequences with all sensor configurations:
./run_script $dataset_name 1
TUM-VI dataset was recorded with two fisheye cameras and an inertial sensor.
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Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.
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Open the script "tum_vi_examples.sh" in the root of the project. Change pathDatasetTUM_VI variable to point to the directory where the dataset has been uncompressed.
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Execute the following script to process all the sequences with all sensor configurations:
./run_script $dataset_name 1