A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “6: We used NDT matching [97], [105] to localize our vehicle in the Nagoya University campus.”
The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection (2009, methodology): “NDT was independently extended to 3D in a 2006 paper by Takeuchi and Tsubouchi [99].”
Voxelized GICP for Fast and Accurate 3D Point Cloud Registration (2020, background): “, multi-resolution [9] and trilinear voxel smoothing [3]).”
Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning (2017, methodology): “In particular, NDT scan matching is considered an efficient method in terms of its memory and computational costs [9], [10], [11].”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “The position and orientation are derived from 3D localization using scan-matching between Velodyne data and the 3D point cloud map [28] (its standard variance ranges from 3cm to 10cm).”