When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates

Abstract

This paper describes the behavior of a commercial light detection and ranging (LiDAR) sensor in the presence of dust. This work is motivated by the need to develop perception systems that must operate where dust is present. This paper shows that the behavior of measurements from the sensor is systematic and predictable. LiDAR sensors exhibit four behaviors that are articulated and understood from the perspective of the shapeof-return signals from emitted light pulses. We subject the commercial sensor to a series of tests that measure the return pulses and show that they are consistent with theoretical predictions of behavior. Several important conclusions emerge: (i) where LiDAR measures dust, it does so to the leading edge of a dust cloud rather than as a random noise; (ii) dust starts to affect measurements when the atmospheric transmittance is less than 71%-74%, but this is quite variable with conditions; (iii) LiDAR is capable of ranging to a target in dust clouds with transmittance as low as 2% if the target is retroreflective and 6% if it is of low reflectivity; (iv) the effects of airborne particulates such as dust are less evident in the far field. The significance of this paper lies in providing insight into how better to use measurements from off-the-shelf LiDAR sensors in solving perception problems. C 2017 Wiley Periodicals, Inc.

Weather Influence and Classification with Automotive Lidar Sensors

“…Comparing these results with the four range measurement behaviors of lidar sensors in the presence of dust, introduced in [15], the influence of fog is similar to dust, in which the measuring range is the front of the dust cloud. Whereas the influence of rain seems to be different.…”

“…In [12] and [15] the influence of dust is analyzed with one type of 2D laser scanner in detail, similar to our approach of studying the influence of fog and rain. Smoke and rain are additionally examined in [12], but since the utilized chamber couldn't produce artificial fog, there are no investigations about the influence of fog.…”

“…Furthermore, rain or fog are influencing the eigenvalues of the covariance matrices of x and y ( f 14,15 ). While the presence of fog and rain is influencing the eigenvalue cov(x), no dependency on eig(cov(y)) can be derived.…”

CNN-Based Lidar Point Cloud De-Noising in Adverse Weather

“…Adverse weather conditions such as fog, rain, dust or snow have a huge impact on the perception of lidar sensors, as shown in [1]- [3], [5], [17]- [23]. Consequently, point cloud processing algorithms either have to deal with these influences, or require preprocessing by filter algorithms.…”

An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

“…Moderate levels of dust are present in Figure a. While LiDAR sensors can “see through” much of the dust using second and later returns reported by the sensor (Phillips, Guenther, & McAree, ), spurious measurements still arise and this, together with the presence of material in flow, makes robust ICP difficult to achieve.…”