FUSE introduces a unified interface for state estimation in SLAM that separates key design choices, with a LiDAR-IMU example showing 1.626m error on a 418m sequence, 7.9% better than Faster-LIO.
LIO- SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping,
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FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
FUSE introduces a unified interface for state estimation in SLAM that separates key design choices, with a LiDAR-IMU example showing 1.626m error on a 418m sequence, 7.9% better than Faster-LIO.