Рет қаралды 12,031
FAST-LIO2 is computationally-efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multi-line spinning and solid-state LiDARs, UAV and handheld platforms, and Intel and ARM-based processors), while still achieving higher or comparable accuracy with existing methods.
Compared with FAST-LIO1, we added many new features:
1. Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz frame rate.
2. Direct odometry on Raw LiDAR points (no feature extraction), achieving better accuracy.
3. Due to the lack of feature extraction, FAST-LIO2 can support different LiDAR Types including spinning (Velodyne, Ouster) and solid-state (Livox Avia, horizon).
4. Support LiDAR built-in or external IMUs.
5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry 4B with 8G RAM.
FAST-LIO2: github.com/hku...
ikd-Tree: github.com/hku...
Paper: github.com/hku...