Abstract
Abstract. LiDAR (Light Detection and Ranging) mounted with static and mobile vehicles has been rapidly adopted as a primary sensor for mapping natural and built environments for a range of civil and military applications. Recently, technology advancement in electro-optical engineering enables acquiring laser returns at high pulse repetition frequency (PRF) from 100Hz to 2MHz for airborne LiDAR, which leads to an increase in the density of 3D point cloud significantly. Traditional systems with lower PRF had a single pulse-in-air zone (PIA) big enough to avoid a mismatch between pulse pair at the receiver. Modern multiple pulses-in-air (MPIA) technology ensures multiple windows of operational ranges for single flight line and no blind-zones; downside of the technology is projection of atmospheric returns closer to same PIA zone of neighbouring ground points and more likely to be overlapping with objects of interest. These characteristics of noise compromise the quality of the scene and encourage usage of noise filtering neural network as existing filters are not effective. A noise filtering deep neural network requires a considerable volume of the diverse annotated dataset, which is expensive. We developed simulation for data augmentation based on physical priors and Gaussian generative function. Our study compares deep learning networks for noise filtering and shows performance gain on 3D U-Net. Then, we evaluate 3D U-Net for simulation-based data augmentation, which shows an increase in precision and F1-score. We also provide an analysis of the underline spatial distribution of points and their impact on data augmentation, and noise filtering.
Highlights
LiDARs have emerged as powerful mapping tools for urban planning, navigation systems, and robotics
Our work demonstrates the performance of various deep neural networks for noise filtering and the impact of simulation-based data augmentation on deep neural network 3D U-Net (Cicek et al, 2016)
Our approach proposed a simulation to generate atmospheric points based on the principals of multiple pulses-in-air (MPIA) and increasing pulse repetition frequency (PRF) to reflect the uniqueness of modern sensor noise
Summary
LiDARs have emerged as powerful mapping tools for urban planning, navigation systems, and robotics. While the atmospheric returns always existed, at lower PRF, we could remove these atmospheric points with simple height filters, or nearest-neighbor algorithms quite effectively. Once they started mixing with the object of interest, more sophisticated algorithms become necessary. Our work demonstrates the performance of various deep neural networks for noise filtering and the impact of simulation-based data augmentation on deep neural network 3D U-Net (Cicek et al, 2016). Figure 1: 3D point cloud from Galaxy T1000 (a) Sideview, (b) Top view; red points represent noise
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