Multipath Interference Suppression of Amplitude-Modulated Continuous Wave Scanning LiDAR Based on Bayesian-Optimized XGBoost Ensemble

TL;DR

This paper introduces a Bayesian-optimized XGBoost ensemble algorithm for suppressing multipath interference in amplitude-modulated continuous wave LiDAR, achieving millimeter-scale error reduction in coaxial scanning systems.

Contribution

It presents a novel MPI suppression method using Bayesian-optimized XGBoost trained on synthetic data, specifically addressing coaxial AMCW LiDAR and achieving high accuracy.

Findings

MPI error reduced from 9.8 mm to less than 2 mm in simulation.

MPI error in real scenes decreased to 2.8 mm, outperforming previous methods.

The approach is effective for coaxial AMCW LiDAR systems.

Abstract

This paper proposes a novel multipath interference (MPI) suppression algorithm based on Bayesian-optimized extreme gradient boosting (XGBoost) ensemble to reduce MPI error in amplitude-modulated continuous wave (AMCW) scanning light detection and ranging (LiDAR). Contrast to this paper, many previous research works have focused on the MPI suppression in conventional AMCW time-of-flight (ToF) sensors with flash type illumination sources. However, the mitigated MPI error of these previous works still remains cm-scale due to the inherent limitation of illumination source and lack of MPI data. Meanwhile, since there exist few previous works for coaxial type AMCW scanning LiDAR, the MPI in such LiDAR still has not been validated. To achieve mm-scale MPI error mitigation regarding aforementioned issues, this paper proposes a MPI error correction algorithm based on Bayesian-optimized XGBoost ensemble and its implementation in coaxial type AMCW scanning LiDAR. To train the XGBoost ensemble, the MPI synthetic dataset generated by customized simulation is used in this paper. According to validation results, the mean absolute error (MAE) of MPI error originally 9.8 mm can be reduced to less than 2 mm by Bayesian-optimized XGBoost in simulation dataset. Such precise MPI mitigation results are also maintained in real object scenes. Specifically, the MAE of MPI error in measurement condition similar with public dataset is reduced to 2.8 mm, which is extremely low compared to other previous works.