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About this paper
Ryan Nemiroff, Kenny Chen, Brett T. Lopez
The authors introduce an estimator that jointly refines gravity and accelerometer intrinsics from a long history of pose and IMU data, then uses gravity constraints in global map optimization. On Newer College’s Cloister dataset, DLIO with the estimator reports 0.1791 m cloud-to-cloud error, versus 0.2608 m without it; this comparison evaluates the integrated systems, not gravity correction alone.
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