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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Abstract
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.
