The interest in LiDAR imaging systems has recently increased in outdoor ground-based applications related to computer vision, in fields like autonomous vehicles and surveillance. However, for the complete settling of the technology, there are still obstacles related to outdoor performance, being its use in adverse weather conditions one of the most challenging. When working in bad weather, data shown in point clouds is unreliable and its temporal behavior is unknown. That is why we have designed, constructed, and tested a scanning-pulsed LiDAR imaging system with outstanding characteristics related to optoelectronic modifications, in particular including digitization capabilities of each of the pulses. Our contribution is related to the digitization, analysis, and characterization of the acquired signal when steering to a target under foggy conditions, as well as the proposal of different strategies to improve point clouds generated in these conditions. The system performance was tested in a macro-scale fog chamber and, using the collected data, two relevant phenomena were identified. Firstly, the backscattering signal of light that first interacts with the media. It is usually responsible for the saturation of the sensor but signal analysis shows it may be easily filtered out. Secondly, false-positive points that appear due to the scattering properties of the media, which only add noise to the point cloud. Digitization of the complete signal can be used to develop algorithms to identify and get rid of them.
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