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FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

📅 January 31, 2022 👤 Wei Xu, Yixi Cai, Dongjiao He et al. 📖 IEEE Transactions on Robotics 📊 1,445 citations

🤖 Plain-English Summary

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Overall, 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 multiline spinning and solid-state LiDARs, unmanned aerial vehicle (UAV) and handheld platforms, and Intel- and ARM-based processors), while...

🔑 Key Findings

  • Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping).
  • The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features.
  • This enables the exploitation of subtle features in the environment and, hence, increases the accuracy.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jan 31, 2022
Journal IEEE Transactions on Robotics
Authors Wei Xu, Yixi Cai, Dongjiao He, Jiarong Lin, Fu Zhang
DOI 10.1109/tro.2022.3141876
Citations 1,445
Source OpenAlex

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