Optical methods can provide measurements without coming into contact with the sample. In the agrifood sector, this feature can be exploited to measure the physical properties of crops. In particular, we focused our research on moisture content (MC) and density estimation. These two physical quantities of the crop are extremely important not only to determine future treatments to be performed, e.g., drying methods and processes but also for estimating the value of the product. In this article, we propose a new model for simultaneous estimation of crop MC and density, using Fourier transform near-infrared spectroscopy combined with partial least square multivariate methods. The model has been developed considering 140 fresh Medicago sativa samples properly harvested. MC ranged from 9.4% to 83.9% whereas density from 46 to 236 kg/m ³ . Reference MC was computed according to the American Society of Agricultural and Biological Engineers standard whereas reference density was determined by estimating the volume of a sample of known mass. The obtained results indicated that crop MC and density information can be recovered from the near-infrared absorption spectrum of the sample with coefficients of determination $R^{2} =0.925$ and $R^{2} =0.681$ for the MC and density, respectively. The mean root mean square relative errors of the estimation were 13.8% and 14.4% for the MC and density, respectively.

LiDARs are becoming one of the pillars for the environmental sensing required by ADAS. Driven by the automotive industry, many new manufacturers are continuously putting new LiDARs on the market, thus increasing their availability and, concomitantly, reducing prices. Accordingly, LiDARs are today finding many new applications also in other fields such as agriculture and industrial automation. In this paper, we describe and discuss a measurement procedure for the analysis and comparison of the performances of LiDARs and, we report an example of the results obtained from the characterization of one the most widespread LiDARs — the VLP 16 by Velodyne. The proposed measurement procedure and set-up have been designed to allow quick and easy installation and analysis of most LiDARs. In particular, they have been designed to allow the straightforward investigation of performances such as the warm-up, stability, errors in the measured coordinates and, parameters such as the spots pattern, waist, and divergence of the laser beams, thus allowing to analyze probably the most relevant performances and parameters for scanning LiDARs. Errors in the measured coordinates have been estimated using an absolute interferometer, whereas the beam parameters have been analyzed using a camera system. As an example, the analysis of the performances of the VLP 16 revealed a warm-up time of about 42 min and errors of few millimeters over a measuring interval of about (2, 21) m. On the other hand, the analysis of the laser beams revealed vertical and horizontal beam divergences of ≈ 1. 2 mrad and ≈ 3. 9 mrad, respectively.

LiDARs are considered essential for the environmental sensing required by most ADAS, including autonomous driving. Such has led to significant investments resulted in the availability of countless measuring systems that are increasingly performing and less expensive. Nevertheless, the extremely high speed of light still leads to a non-negligible quantization error in the direct time-of-flight (ToF) measure at the base of pulsed LiDARs — the leading technology for automotive applications. Hence, pulsed 3D-LiDARs analyze the surrounding by approximating and deforming it on concentric spheres whose radii are quantized with a quantization step that, for most commercial systems, is on the order of some centimeters. The deformation and error introduced by such quantization can thus be significant. In this study, we point out the approximations and assumptions intrinsic to 3D-LiDARs and propose a measurement procedure that, through the analysis of the fine variations of the target position, allows an accurate investigation of the axial resolution and error — probably among the few limitations still affecting this technology. To the best of our knowledge, this is the first study focused on the detailed analysis of the quantization error in 3D-LiDARs. The proposed method has been tested on one of the most popular 3D-LiDARs — namely the MRS 6000 by Sick. The obtained results revealed for the MRS 6000 a quantization step of about 6 cm (ToF quantization of about 0.4 ns) and an axial error normally distributed with experimental standard deviation of about 30 mm.

3D-LiDARs are nowadays used for many applications, the success of which certainly depends on the processing of the LiDAR output—the point cloud, PC,—but it also inexorably depends on the quality of the PC data. In this study, we propose an experimental method aimed at allowing estimating the errors and deformations that will statistically affect the LiDAR output — the PC. Taking advantage of the fact that LiDARs sample the surrounding space by observing it along divergent lines, hereinafter referred to as rays, this study proposes a simple method based on the experimental determination of the ray detection probability — the probability that a single ray detects the hit object, or a fraction of it, by adding a point in the point cloud. All other probabilities of interest are derived from such a probability. The proposed method also allows highlighting unexpected errors such as cross-talk. As will be shown by the examples given, due to cross-talk, small objects may be deformed and enlarged on a significantly greater number of points in the PC. Likewise, objects angularly separated by an angle greater than the angular resolution declared by the manufacturer may unexpectedly result in a continuum of points. Such errors may compromise the ability to perform very important tasks such as detection, classification, and tracking of dynamic and static objects, as well as the partition of the scene into drivable and non-drivable regions and the path planning around generic obstacles in 3D space.

The vast multitude of LiDAR systems currently available on the market makes the need for methods to compare their performances increasingly high. In this study, we focus our attention on the development of a method for the analysis of the effects induced by the fog, one of the main challenges for Advanced Driver Assist Systems (ADASs) and autonomous driving. Large experimental setups capable of reconstructing adverse weather conditions on a large scale in a controlled and repeatable way are certainly the best test conditions to analyze and compare LiDARs performances in the fog. Nonetheless, such large plants are extremely expensive and complex, therefore only available in a few sites in the world. In this study, we thus propose a measurement method, a data analysis procedure and, an experimental setup that are extremely simple and inexpensive to implement. The achievable results are reasonably less accurate than those obtainable with large plants. Nevertheless, the proposed method can allow to easily and quickly obtain a preliminary estimate of the performance in the presence of fog and a rapid benchmarking of different LiDAR systems.

The use of LiDARs in automotive is increasingly widespread. In order to operate in a critical environment such as that of mobility, these systems must offer increasingly high performance. In particular, the ability to estimate the position of objects regardless of their reflectance and presence of diffusing backgrounds is a very sought-after feature by manufacturers. In commercial systems various strategies are used to make the measurement as insensitive as possible to these effects, however, it is not possible to fully compensate for the measurement errors caused by them. In this paper, we propose two simple experimental setups to verify the presence of these measurement errors in two scenarios. Moreover, we report the performance of a commercial LiDAR (MRS 6000 by Sick) using certified reflectance standards (Spectralon® Diffuse Reflectance Standards, by Labsphere). For this LiDAR, the results obtained show that a logarithmic variation of the reflectance of the target of 1.26-log at a target distance 2.4 m provides incompatible measurements. Furthermore, the presence of a background at a distance shorter than 11 cm, 12 cm and 13 cm respectively with 50 %, 75 % and 99 % reflectance also provides incompatible measurements for a target distance of 1.3 m from the LiDAR.