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koide3 committed Jul 1, 2024
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# gtsam_points

This is a collection of GTSAM factors and optimizers for range-based SLAM.
This is a collection of [GTSAM](https://gtsam.org/) factors and optimizers for range-based SLAM.

Tested on Ubuntu 22.04 and CUDA 12.2 / NVIDIA Jetson Xavier and Orin (JetPack 5.0.1) with **GTSAM 4.2a9**.

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## Optimizers for GPU Factors

All the following optimizers were derived from the implementations in GTSAM
All the following optimizers were derived from the implementations in GTSAM.

- **LevenbergMarquardtOptimizerExt**
- **ISAM2Ext**
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## Nearest Neighbor Search
- **KdTree**
KdTree with parallel tree construction. Derived from [nanoflann](https://github.com/jlblancoc/nanoflann).
- **iVox**
Incremental voxel-based nearest neighbor search [[11]](#IVOX).
- **IncrementalVoxelMap**
Incremental voxel-based nearest neighbor search (iVox) [[11]](#IVOX).
- **IncrementalCovarianceVoxelMap**
Incremental voxelmap with online normal and covariance estimation.


## Continuous-Time Trajectory (Under development)
- **B-Spline**
Cubic B-Spline-based interpolation and linear acceleration and angular velocity expressions [[12]](#BSPLINE_D)
Cubic B-Spline-based interpolation and linear acceleration and angular velocity expressions [[12]](#BSPLINE_D).
- **ContinuousTrajectory**
Cubic B-Spline-based continuous trajectory representation for offline batch optimization
Cubic B-Spline-based continuous trajectory representation for offline batch optimization.


## Installation
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## Build gtsam_points
git clone https://github.com/koide3/gtsam_points --recursive
mkdir gtsam_points/build && cd gtsam/build
mkdir gtsam_points/build && cd gtsam_points/build
cmake .. -DCMAKE_BUILD_TYPE=Release

# Optional cmake arguments
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# -DBUILD_WITH_SYSTEM_EIGEN=ON

make -j$(nproc)
sudo make install
```

## Demo
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./build/demo_bundle_adjustment
./build/demo_continuous_time
./build/demo_continuous_trajectory
./build/demo_colored_registration
```

## Videos

- [Multi-scan registration of 5 frames (10 factors)](https://youtu.be/HCXCWlx_VOM)
- [Multi-scan registration of 5 frames (= A graph with 10 registration factors)](https://youtu.be/HCXCWlx_VOM)
- [Bundle adjustment factor](https://youtu.be/tuDV0GCOZXg)
- [Continuous-time ICP factor](https://youtu.be/Xv2-qDlzQYM)
- [Colored ICP factor](https://youtu.be/xEQmiFV79LU)
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