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DAVIDE CASSANELLI

Assegnista di ricerca
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2023 - A Simple Setup for the Experimental Verification of Measurement Artifacts Introduced by 3D-LiDAR in in-motion Acquisitions [Relazione in Atti di Convegno]
Cassanelli, Davide; Cattini, Stefano; Medici, Lorenzo; Ferrari, Luca; Rovati, Luigi
abstract

2023 - A method for estimating object detection probability, lateral resolution, and errors in 3D-LiDARs [Articolo su rivista]
Cattini, S.; Cassanelli, D.; Ferrari, L.; Rovati, L.
abstract

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.

2023 - A method for the estimate erroneous fog detection in automotive LiDAR [Relazione in Atti di Convegno]
Cassanelli, Davide; Cattini, Stefano; Ferrari, Luca; Rovati, Luigi
abstract

2023 - Image analysis algorithm for the Anterior Chamber Angle Closure estimation and Van Herick classification [Relazione in Atti di Convegno]
Cassanelli, Davide; Gibertoni, Giovanni; Ferrazza, Manuela; Tramarin, Federico; Tanga, Lucia; Quaranta, Luciano; Oddone, Francesco; Rovati, Luigi
abstract

2022 - A simple method for the preliminary analysis and benchmarking of automotive LiDARs in fog [Relazione in Atti di Convegno]
Cassanelli, D.; Cattini, S.; Di Loro, G.; Di Cecilia, L.; Ferrari, L.; Goldoni, D.; Rovati, L.
abstract

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.

2022 - Assessment of a Vision-Based Technique for an Automatic Van Herick Measurement System [Articolo su rivista]
Fedullo, T.; Cassanelli, D.; Gibertoni, G.; Tramarin, F.; Quaranta, L.; Riva, I.; Tanga, L.; Oddone, F.; Rovati, L.
abstract

The adoption of Artificial Intelligence methods within the instrumentation and measurements field is nowadays an attractive research area. On the one hand, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a convenient and effective solution in many modern research areas. On the other hand, AI allows for the compensation of inaccurate or not complete models of specific phenomena or systems. In this context, this paper investigates the possibility to exploit suitable Machine Learning techniques in a vision-based ophthalmic instrument to perform automatic Anterior Chamber Angle (ACA) measurements. In particular, two CNN–based networks have been identified to automatically classify acquired images and select the ones suitable for the Van–Herick procedure. Extensive clinical trials have been conducted by clinicians, from which a realistic and heterogeneous image dataset has been collected. The measurement accuracy of the proposed instrument is derived by extracting measures from the images of the aforementioned dataset, as well as the system performances have been assessed with respect to differences in patients’ eye color. Currently, the ACA measurement procedure is performed manually by appropriately trained medical personnel. For this reason, Machine Learning and Vision–Based techniques may greatly improve both test objectiveness and diagnostic accessibility, by enabling an automatic measurement procedure.

2022 - LiDARs detected signal and Target distance estimation: measurement errors from Target reflectance and multiple echos [Relazione in Atti di Convegno]
Cassanelli, D.; Cattini, S.; Di Loro, G.; Di Cecilia, L.; Ferrari, L.; Rovati, L.
abstract

2022 - Training of an artificial intelligence algorithm for automatic detection of the Van Herick grade [Relazione in Atti di Convegno]
Cassanelli, Davide; Fedullo, Tommaso; Gibertoni, Giovanni; Saporito, Giorgia; Ferrazza, Manuela; Oddone, Francesco; Riva, Ivano; Quaranta, Luciano; Tramarin, Federico; Rovati, Luigi
abstract

2021 - A Machine Learning Approach for a Vision-Based Van-Herick Measurement System [Relazione in Atti di Convegno]
Fedullo, Tommaso; Cassanelli, Davide; Gibertoni, Giovanni; Tramarin, Federico; Quaranta, Luciano; de Angelis, Giovanni; Rovati, Luigi
abstract

2021 - A new screening system for the estimation of ocular anterior chamber angle width [Relazione in Atti di Convegno]
Cassanelli, D.; Gibertoni, G.; Cattini, S.; Quaranta, L.; Riva, I.; Bruttini, C.; de Angelis, G.; Rovati, L.
abstract

Primary Angle Closure Glaucoma occurs more frequently in people with a narrower limbal anterior chamber depth (LACD) condition. Nowadays, clinical gold standard as an assessment technique, i.e. gonioscopy, is invasive and complex, whereas Van Herick (VH) technique is non-invasive, but subjective. The instrument, we propose, can automatically performs the VH procedure using a blue laser line, a piezo-actuator, and an image recognition algorithm embedded on a Raspberry Pi board. Preliminary measurements have been carried out on volunteers, and the results proved the feasibility of our approach. The final instrument unveils a high potential for early-stage diagnosis and screening applications.

2021 - A procedure for the characterization and comparison of 3D-LiDAR systems [Articolo su rivista]
Cattini, S.; Cassanelli, D.; Di Cecilia, L.; Ferrari, L.; Rovati, L.
abstract

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.

2021 - Analysis, quantification, and discussion of the approximations introduced by pulsed 3-D lidars [Articolo su rivista]
Cattini, S.; Cassanelli, D.; Di Loro, G.; Di Cecilia, L.; Ferrari, L.; Rovati, L.
abstract

Light detection and rangings (LiDARs) are considered essential for the environmental sensing required by most advanced driver assistance system (ADAS), including autonomous driving. This 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 nonnegligible quantization error in the direct time-of-flight (ToF) measure at the base of pulsed LiDARs—the leading technology for automotive applications. Hence, pulsed 3-D 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 3-D 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 3-D LiDARs. The proposed method has been tested on one of the most popular 3-D 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.

2021 - Comparison of VLP-16 and MRS-1000 LiDAR systems with absolute interferometer [Relazione in Atti di Convegno]
Cassanelli, D.; Cattini, S.; Di Loro, G.; Di Cecilia, L.; Ferrari, L.; Rovati, L.
abstract

Nowadays, LiDARs hold a relevant place in providing the environmental sensing required by most ADAS. Promoted by such increasing demand, many new manufacturers are emerging and, new LiDARs are continuously made available on the market. If, on the one hand, the availability of LiDARs with increasing performance and reducing cost has brought significant benefits also promoting the spread of such measuring systems in other areas such as industrial controls and agriculture, on the other, it has made it more difficult to extricate in the immense set of LiDARs present on the market today. In response to this growing need for standards and methods capable of comparing the various LiDARs, many international standards and scientific publications are being produced on the subject. In this paper, we continue our work on LiDARs characterization, focusing our attention on comparing the performances of two of the most popular systems - namely, the MRS 1000 by Sick and the VLP 16 by Velodyne. Starting from the analysis of the warm-up time and stability, such a comparison focused on analyzing the axial error of both systems. Such errors have been estimated by exploiting a custom rail system and an absolute interferometer. The obtained results revealed warm-up times of a few tens of minutes and maximum absolute axial errors of a few centimeters in the range [1.5, 21] m.

2021 - Partial Least Squares Estimation of Crop Moisture and Density by Near-Infrared Spectroscopy [Articolo su rivista]
Cassanelli, D.; Lenzini, N.; Ferrari, L.; Rovati, L.
abstract

Optical methods can provide measurements without coming into contact with the sample. In the agrifood sector, this feature can be exploited to measure physical properties of crops. In particular, we focused our research on moisture content 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 moisture content 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. Moisture content ranged from 9.4% to 83.9% whereas density from 46 kg/m3 to 236 kg/m3. Reference MC was computed according to the American Society of Agricultural and Biological Engineers standard whereas reference density was determined estimating the volume of a sample of known mass. The obtained results indicated that crop moisture content and density information can be recovered from the near-infrared absorption spectrum of the sample with coefficients of determination R2 = 0.925 and R2 = 0.681 for the moisture content and density, respectively. Mean root mean square relative errors of the estimation were 13.8% and 14.4% for the moisture content and density, respectively.