**5. Conclusions**

Over recent years, LIDAR technology has become a *panacea* in the fields of optomechanical engineering and optoelectronics. It especially seems to hold a relevant role in novel applications related to outdoor environments. One of the keys to its success is the amount of information it can provide despite relying on a very simple method. Being based on a simple working principle (counting elapsed time between events in magnitudes carried out by light, e.g., reflected energy from a pulse of light sent to a target), it allows the performance of complete 3D mapping with outstanding characteristics.

However, for the complete settling of the technology, there are still obstacles pending to be solved. One of the most challenging is related to its outdoor performance. As with any other optical sensor, under the presence of adverse weather conditions, such as fog, the system performance is heavily altered and the quality of the detection becomes severely degraded.

Usually, commercial systems are like black boxes, only returning the point cloud or 3D map. When facing bad weather, data are unreliable and its behavior is unknown. However, the LiDAR system can provide a lot more information, which might enable optimized features.

In order to propose reasonable solutions, the knowledge of optical physics behind the phenomena degrading the performance of the system may be of significant interest. The propagation of light through scattering media, such as adverse weather conditions, shows two main problems: the saturation of the sensor and the attenuation of the pulse. Both are related to the dispersive effect and absorption characteristics of the media. Due to their nature, both phenomena can be easily studied with models if the optical properties of the media are known.

Using Mie Theory, and taking into necessary conditions, we can obtain an approximation of the media properties (absorption coefficient, scattering coefficient, phase

function, and anisotropy factor). The derivation of the complete theory may be long and tedious; however, its application is straightforward.

Once all the media properties are derived, they can be used to solve the radiation transfer theory. Essentially, it is the basic theory that allows the calculation of light distributions in multiple scattering media with absorption. Its core is the radiation transfer equation, which computes the balance that characterizes the flow of photons in a given volume element. However, the analytical solution to this equation is complicated and often impossible to solve when boundaries, inhomogeneities, or nonstationary effects are involved. The Monte-Carlo (MC) method is used as the conventional tool to arrive at a statistical solution in these cases. By using the MC method, we can solve the RTT of a pulsed LiDAR when working through a turbid medium. Along this chapter, we have shown how the model succeeds in modeling different kinds of scenarios: a media where absorption prevails over scattering, predictions of pulsed light interactions, dynamics of backscattering, light response to different kinds of objects and media, etc. As a result, we are able to predict the behavior of our system in the different scenarios where it breaks down. For example, it is possible to estimate the range of the system, the response toward different objects or the characteristics of the blinding backscattering.

The next step would be related to studying other features that are considered very relevant when designing LiDAR imaging systems, for example:


Moreover, one has to not lose focus on the final implementation of the system. Simulations can be used to guide us, to try exotic ideas without the need to build up the system. However, experimental results are always expected as the final product of this whole process.
