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Machine learning-aided LiDAR range estimation

Bastos, D. ; Monteiro, P. ; Oliveira, A. ; Drummond, M. V.

Optics Letters Vol. 48, Nº 7, pp. 1962 - 1962, March, 2023.

ISSN (print): 0146-9592
ISSN (online): 1539-4794

Scimago Journal Ranking: 1,18 (in 2022)

Digital Object Identifier: 10.1364/OL.487000

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Automotive light detection and ranging (LiDAR) requires accurate and computationally efficient range estimation methods. At present, such efficiency is achieved at the cost of curtailing the dynamic range of a LiDAR receiver. In this Letter, we propose using decision tree ensemble machine learning models to overcome such a trade-off. Simple and yet powerful models are developed and proven capable of performing accurate measurements across a 45-dB dynamic range.