In recent times, there has been a surge of interest in LiDAR imaging systems, particularly in outdoor terrestrial applications associated with computer vision. However, a significant hurdle preventing their widespread implementation lies in their limited tolerance for adverse weather conditions, such as fog. To address this challenge, researchers have explored the capability of polarization in improving detection capabilities in such media. This paper explores the potential of LiDAR technology to obtain polarized images through fog and investigates the impact of fog on object detection using digitized temporal signals and point clouds. The study utilizes a LiDAR-polarized imaging system using circular polarization, which has been shown to enhance image contrast in highly-dispersive media. The analysis of the polarimetric information of the backscattered light signal in fog reveals its influence on object detection and evaluates the range difference between orthogonal polarimetric channels: coplanar and cross-configuration. The results demonstrate that cross-configuration detection provides larger range and more detailed point clouds compared to co-planar configuration, particularly benefiting metallic objects, for the same foggy conditions. By utilizing circularly polarized incident light and cross-configuration detection, the LiDAR system can improve the signal-to-noise ratio by filtering out the co-polarized fog responses. However, the range of the system may be reduced compared to non-polarized detection. Overall, our findings indicate that utilizing a cross-polarization detection setup can effectively reduce the impact of fog backscatter while preserving the return signal from objects of interest in the majority of cases.
KEYWORDS: Point clouds, LIDAR, Backscatter, Visibility through fog, Imaging systems, Signal processing, Signal detection, Object detection, 3D-TOF imaging
The interest in LiDAR imaging systems has recently increased in outdoor ground-based applications related to computer vision, in fields like autonomous vehicles. 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. 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. The system performance was tested in a macro-scale fog chamber and, using the collected data, two relevant phenomena were identified: the backscattering signal of light that first interacts with the media and false-positive points that appear due to the scattering properties of the media. Digitization of the complete signal can be used to develop algorithms to identify and get rid of them. 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.
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