Rafael Corsino González de Los Reyes

Noise contamination in three-dimensional data remains a persistent challenge in reconstruction and measurement pipelines, with critical implications for dimensional inspection and industrial metrology, as well as for autonomous navigation and cultural heritage documentation. Whenever a point cloud is used to measure rather than merely to visualize, the noise it carries propagates directly into the reported dimension and into its associated uncertainty. This work presents an innovative three-axis taxonomy that unifies the classification and mitigation of noise in 3D point clouds. Our framework systematically categorizes noise sources into (1) statistical models, (2) scene interferences, and (3) reconstruction imperfections, and it is designed so that any newly encountered noise type can be assigned consistently by means of three explicit criteria (origin, modeling tractability and spatio-temporal dependency). Through theoretical analysis along these three axes, we discuss the fundamental limitations, practical challenges, and adaptive strategies of each method and summarize the resulting cross-category applicability in a comparative table. Among the unresolved challenges are real-time processing of dynamic scenes, separation of overlapping noise types, and biases in learning models toward specific sensor geometries. We further argue that, in a measurement context, a denoising operator must be assessed not only by the noise it suppresses but also by the systematic surface displacement—the bias—it introduces, which aggregate distance metrics tend to conceal. This research consolidates fragmented knowledge into actionable guidelines, equipping metrology and inspection practitioners to design robust 3D measurement systems and guiding researchers toward impactful innovations in noise-adaptive sensing.

Ensuring the surface quality of industrial components requires the detection of small superficial defects—such as cracks, bumps, and peaks—using high-resolution 3D sensors. However, training machine learning algorithms for this task is constrained by the limited availability of annotated 3D defect datasets. In this work, we propose a method for generating synthetic 3D datasets of surface defects using Free-Form Deformation applied to CAD models. The technique allows localized insertion of parametrized defects adapted to the object’s geometry and supports diverse defect types through customizable elevation maps. To simulate realistic sensor acquisition, we replicate the scanning process of a profilometric 3D sensor, including surface and sensor noise. The output consists of labeled depth images that closely resemble real-world sensor data. We validate the approach by training object detection networks on synthetic datasets and evaluating their performance on real samples, demonstrating comparable accuracy. The proposed method reduces reliance on rare and costly real defect data, offering a scalable tool for developing and testing surface inspection systems in industrial contexts.

High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness.

High-precision surface defect detection in manufacturing is essential for ensuring quality control. Laser triangulation profilometric sensors are key to this process, providing detailed and accurate surface measurements over a line. To achieve a complete and precise surface scan, accurate relative motion between the sensor and the workpiece is required. It is crucial to control the sensor pose to maintain optimal distance and relative orientation to the surface. It is also important to ensure uniform profile distribution throughout the scanning process. This paper presents a novel Reinforcement Learning (RL) based approach to optimize robot inspection trajectories for profilometric sensors. Building upon the Boustrophedon scanning method, our technique dynamically adjusts the sensor position and tilt to maintain optimal orientation and distance from the surface, while also ensuring a consistent profile distance for uniform and high-quality scanning. Utilizing a simulated environment based on the CAD model of the part, we replicate real-world scanning conditions, including sensor noise and surface irregularities. This simulation-based approach enables offline trajectory planning based on CAD models. Key contributions include the modeling of the state space, action space, and reward function, specifically designed for inspection applications using profilometric sensors. We use Proximal Policy Optimization (PPO) algorithm to efficiently train the RL agent, demonstrating its capability to optimize inspection trajectories with profilometric sensors. To validate our approach, we conducted several experiments where a model trained on a specific training piece was tested on various parts in simulation. Also, we conducted a real-world experiment by executing the optimized trajectory, generated offline from a CAD model, to inspect a part using a UR3e robotic arm model.

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor’s movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.

Usual human motion capture systems are designed to work in controlled laboratory conditions. For occupational health, instruments that can measure during normal daily life are essential, as the evaluation of the workers' movements is a key factor to reduce employee injury- and illness-related costs. In this paper, we present a method for joint angle measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors. This method estimates wrist flexion, wrist lateral deviation, elbow flexion, elbow pronation, shoulder flexion, shoulder abduction and shoulder internal rotation. The algorithms avoid numerical integration of the signals, which allows for long-time estimations without angle estimation drift. The system has been tested both under laboratory and field conditions. Controlled laboratory tests show mean estimation errors between 0.06° and of 1.05°, and standard deviation between 2.18° and 9.20°. Field tests seem to confirm these results when no ferromagnetic materials are close to the measurement system.

During a survey of nematode associates of ambrosia beetles from dead and dying red bay and avocado trees affected by the laurel wilt epidemic in southern Florida, aRuehmaphelenchusspecies was isolated from the non-native ambrosia beetle,Xylosandrus crassiusculus. The new species is characterised by its possession of an oral disc at the stoma opening, three lines in the lateral field, male spicule with clear dorsal and ventral limbs connected by elongated triangular cuticle, thin membrane-like tissue and cuticular bridge-like structure, conical tail with pointed tip of males and conical tail with digitate mucro of females. The new species is very similar to four previously described species:R. asiaticus,R. digitulus,R. thailandaeandR. sirisus, and can be distinguished only by some minor morphological differences in male tail characters,i.e., spicule morphology, position of genital papillae and tail tip shape, and morphometric values. However, the new species is phylogenetically unique,i.e., it is the basal taxon of theRuehmaphelenchusclade and close toBursaphelenchusspp.Ruehmaphelenchus juliaen. sp. is therefore proposed based on its morphological diagnostic characters and molecular sequences of near-full-length of SSU, internal transcribed spacer region, D1, D2 and D3 expansion segments of LSU ribosomal RNA and partial mitochondrial COI genes.

In this paper we propose an approach for the estimation of the slope of the walking surface during normal walking using a body-worn sensor composed of a biaxial accelerometer and a uniaxial gyroscope attached to the shank. It builds upon a state of the art technique that was successfully used to estimate the walking velocity from walking stride data, but did not work when used to estimate the slope of the walking surface. As claimed by the authors, the reason was that it did not take into account the actual inclination of the shank of the stance leg at the beginning of the stride (mid stance). In this paper, inspired by the biomechanical characteristics of human walking, we propose to solve this issue by using the accelerometer as a tilt sensor, assuming that at mid stance it is only measuring the gravity acceleration. Results from a set of experiments involving several users walking at different inclinations on a treadmill confirm the feasibility of our approach. A statistical analysis of slope estimations shows in first instance that the technique is capable of distinguishing the different slopes of the walking surface for every subject. It reports a global RMS error (per-unit difference between actual and estimated inclination of the walking surface for each stride identified in the experiments) of 0.05 and this can be reduced to 0.03 with subject-specific calibration and post processing procedures by means of averaging techniques.