Full Waveform LiDAR for Adverse Weather Conditions

Abstract

We present and discuss the case for full waveform pixel and image acquisition and processing to enable LiDAR sensors to penetrate and reconstruct 3D surface maps through obscuring media. To that end, we review work on signal propagation, on scanning and arrayed sensors, on signal processing strategies for independent pixels and employing spatial context, on reducing complexity and accelerating processing by sensor design, algorithmic changes, compressed sensing, and parallel processing. We report several experimental studies on LiDAR imaging through complex media, and how these can inform the automotive LiDAR scenario. We conclude with a discussion of future development and potential for full waveform LiDAR (FWL).

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

“…The former is a common option for automotive manufacturers due to its reliability, eye protection, and cost-effectiveness with silicone detectors. In [ 100 , 101 ], an authoritative analysis on lidar, its waveform, and market penetration strategies are discussed. Lidars provide a good physical description of the target and, due to that, lidars have been used for target detection, tracking, and motion prediction., Filtering of the ground and clustering of the target [ 102 , 103 , 104 , 105 ] are two methods widely used by lidar for object detection, which provide the spatial information of the target.…”

300 GHz radar object recognition based on deep neural networks and transfer learning

“…LiDAR A 32 channel, 10Hz, Velodyne HDL-32e LiDAR [199] is used to give 360 o coverage. Since the LiDAR signal can be severely attenuated and reflected by intervening fog or snow [190], the data can be missing, noisy and incorrect. An example of LiDAR in snow can be seen in Figure 6.5.…”

“…Camera and LiDAR are the two primary perceptual sensors that are usually adopted. However, since these are visible spectrum sensors, the data be affected dramatically by bad weather due to attenuation, multiple scattering and turbulence [28,[187][188][189][190], most existing datasets were captured Chapter 6. A Multi-Modal Object Recognition Dataset for Autonomous Cars in Adverse Weather in good weather.…”

Combining automotive radar and LiDAR for surface detection in adverse conditions

“…In this article, our contribution is a new algorithmic approach to combine longitudinal (range) laser and radar reflections to find robustly the true surface reflection in adverse conditions. This requires full waveform analysis in which the properties of the obscuring media are modelled [13]. The position in the longitudinal return defines the range; the azimuthal and elevation coordinates are defined by the pointing angle of the LiDAR signal, as is always the case.…”