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Learning inertial odometry from raw IMU data without position labels allows for unprecedented scalability and efficiency in training.
Achieve real-time, high-accuracy LiDAR-visual-inertial odometry by ditching marginalization for a pose-only bundle adjustment, outperforming existing methods on extensive datasets.
