FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

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...

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• 1 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).
• 2 The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features.
• 3 This enables the exploitation of subtle features in the environment and, hence, increases the accuracy.

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

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