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support for Scan context loop closure

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2021-07-14 06:51:30 -04:00
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3. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
4. [R2LIVE](https://github.com/hku-mars/r2live): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
5. [UGV Demo](https://www.youtube.com/watch?v=wikgrQbE6Cs): Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.
6. [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM#for-livox-lidar): A [Scan-Context](https://github.com/irapkaist/scancontext) loop closure module that can directly work with FAST-LIO1 (The support for FAST-LIO2 is under developing).
6. [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM#for-livox-lidar): A [Scan-Context](https://github.com/irapkaist/scancontext) loop closure module that can directly work with FAST-LIO.
## FAST-LIO
**FAST-LIO** (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package address many key issues:
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**New Features:**
1. Incremental mapping using [ikd-Tree](https://github.com/hku-mars/ikd-Tree), achieve faster speed and over 100Hz LiDAR rate.
2. Direct odometry (scan to map) on Raw LiDAR points (feature extraction can be closed), achieving better accuracy.
3. Since no need for feature extraction, FAST-LIO2 can support different LiDAR Types including spinning (Velodyne, Ouster) and solid-state (Avia, horizon) LiDARs, and can be easily extended to support more LiDAR.
2. Direct odometry (scan to map) on Raw LiDAR points (feature extraction can be disabled), achieving better accuracy.
3. Since no requirements for feature extraction, FAST-LIO2 can support may types of LiDAR including spinning (Velodyne, Ouster) and solid-state (Livox Avia, Horizon, MID-70) LiDARs, and can be easily extended to support more LiDARs.
4. Support external IMU.
5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry Pi 4B(8G RAM).
@@ -121,7 +121,7 @@ Edit ``` config/velodyne.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ``` (both internal and external, 6-aixes or 9-axies are fine)
3. Line number (we tested 16 and 32 line, but not tested 64 or above): ``` scan_line ```
3. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): ``` scan_line ```
4. Translational extrinsic: ``` extrinsic_T ```
5. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).