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The implemented algorithm consists in three steps.
The implemented algorithm is demonstrated on actual manufacturing data, as well as on some pathological examples.
Since the implemented algorithm is limited by memory bandwidth, we concentrate on improving memory access.
The matching between the implemented algorithm and the technology used is discussed.
Some problems in the implemented algorithm are identified and effective solutions are proposed.
The implemented algorithm is asymptotically optimal O(N) both in CPU time and memory usage with optimized prefactors.
We demonstrate the implemented algorithm on three design scenarios involving spatial pairs and planar pairs with axis misalignment.
Further, the implemented algorithm classifies points from the LiDAR measurements which correspond to the canopy using a supervised classifier.
The paper presents, as a case of study, a fully implemented algorithm used for turning lathe machining of shoe lasts.
The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.
Then, calcium phosphate precipitation which is of major interest for P-recovery from wastewater, has been chosen as an illustration of the implemented algorithm.
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