From a paper in MDPI by Lian et al.
Considering the fact that collecting LIDAR data in adverse weather like dust storms is a formidable task, we
propose a novel data augmentation framework based on physical simulation. Our model takes into
account finite laser pulse width and beam divergence.
The discrete dusty particles are distributed randomly in the surrounding of LIDAR sensors. The attenuation effects of scatters are represented implicitly with extinction coefficients. The coincidentally returned echoes from multiple particles are evaluated by explicitly superimposing their power reflected from each particle.
Based on the above model, the position and intensity of real point clouds collected from dusty weather can be modified.
Numerical experiments are provided to demonstrate the effectiveness of the method.
Through analyzing physically simulated point clouds in different dusty weather, such as floating dust, blowing sand and dust storm, we verify the reliability and effectiveness of our algorithm. The synthetic data generated by our simulator can be used as training data to enhance the accuracy and robustness of an object detector in dusty weather, thus alleviating the difficulty of collecting real dusty point clouds.
Although our method is applied to dusty weather, it can also be used in other adverse weather conditions (snowfall, rain, etc.) with trivial modification. Our work holds promise in practical applications of autonomous driving and robotics. We expect that this work could advance further research on intelligent perception systems in adverse weather conditions.
For the full paper ON lidar point cloud augmentation CLICK HERE.
