Recently, significant efforts have been made to apply autonomy to off-road vehicles and machinery. For this, a LiDAR sensor has played an important role in a variety of related applications due to its merits of providing high-resolution and accurate information about the environment. However, its detection performance significantly degrades under dusty conditions. Specifically, measured data can be corrupted due to light backscattering from the dust particles, and thus it makes the whole perception of the vehicles prone to failure.

To deal with this problem, we designed a de-dust filter using a LIOR (Low-Intensity Outlier Removal) filtering technique that offers a viable solution to eliminate dust particles from measurement data. The proposed method employs a two-step filtering procedure. The first step is based on the fact that dust particles have a lower intensity than other non-dust objects. A threshold intensity was identified by analyzing the gathered data, which can be used to distinguish dust from non-dust objects. Then, points with intensity values below the threshold were eliminated through this filtering process. As a second step, a statistical outlier removal filter was applied to the points identified as outliers in the previous step in order to preserve non-dust object points that had low intensity but were incorrectly classified as dust.

Experimental results confirm that the proposed method is robust to dust particles by successfully removing them from the measured point cloud with good filtering accuracy while maintaining rich information about the environment. Therefore, this method can be applied to LiDAR sensors mounted on vehicles in various industrial fields with dust exposure, such as construction, mining, and agriculture.