When the dust settles: the four behaviors of LiDAR in the presence of fine airborne particulates

Phillips, Tyson Govan, Guenther, Nicky and Mcaree, Peter Ross (2017) When the dust settles: the four behaviors of LiDAR in the presence of fine airborne particulates. Journal of Field Robotics, . doi:10.1002/rob.21701


Author Phillips, Tyson Govan
Guenther, Nicky
Mcaree, Peter Ross
Title When the dust settles: the four behaviors of LiDAR in the presence of fine airborne particulates
Journal name Journal of Field Robotics   Check publisher's open access policy
ISSN 1556-4967
1556-4959
Publication date 2017-01-01
Sub-type Article (original research)
DOI 10.1002/rob.21701
Open Access Status Not yet assessed
Total pages 25
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Collection year 2018
Language eng
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 shape-of-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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
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