The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection

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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 ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research (2016, unknown): “The autonomous alignment stage registers the prerecorded point cloud of the shelf wrt. to the current point cloud provided by the sensor by NDT [19].”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, methodology): “Having this downsampled point cloud, the Normal Distributions Transform (NDT) algorithm [11] can obtain the relative position between two point clouds [12].”
Fast LIDAR localization using multiresolution Gaussian mixture maps (2015, background): “This has been applied to ICP [18], NDT [19] [20], and occupied voxel lists [21].”