In this paper, 6D camera pose refinement is explored using 3D Gaussian Splatting (3DGS) on lightly textured industrial object datasets. The study employs datasets generated with Unity 3D rendering software, featuring objects such as a bicycle, MiR robot, Tiago robot, and UR robotic arm, each captured with ground-truth intrinsic and extrinsic camera parameters. A 3DGS model is trained to represent each scene, and 6D pose refinement is evaluated using a recent pose optimization approach, iComMa (inverting 3DGS via Comparing and Matching), which aligns rendered and query images. Experiments utilize 20% of the images for testing and 80% for training the 3DGS models. Camera poses are initialized with varying degrees of perturbation (δ) in both rotation and translation to assess the refinement capabilities. Robustness is further evaluated under degraded conditions by applying various types of noise to the query images, including Gaussian noise, salt-and-pepper noise, dilation, and erosion. Results demonstrate reliable pose refinement under photometric noise; however, with structural noise, the method maintains good rotation accuracy but struggles with translation due to changes in geometric features. This approach shows promise for industrial applications, where 3DGS models trained on synthetic datasets can refine camera poses in real-world industrial environments with common noise characteristics.

Within the framework of Industry 5.0, affordances enable intuitive and adaptive interactions between operators and their industrial work environments. Accurately perceiving these affordances enhances overall production performance, safety, and operator effectiveness. This paper focuses on the initial step of a larger affordance characterization pipeline: detecting tools used by operators during manual assembly tasks. To address the challenges of data scarcity and annotation effort in industrial contexts, we train a custom YOLOv9-based deep learning model on a data-augmented dataset combining real-world and synthetic images, automatically generated from a digital model of an industrial workstation in Unity3D. Through extensive experiments, we varied dataset sizes (50–300 images) and real-world data proportions in the data-augmented train datasets (0%–50%), to assess their impact on tool detection. Results show that only 10% of real-world data is sufficient to achieve strong performance across all data-augmented dataset sizes. A tool specific analysis reveals that visual characteristics such as size and shape influence detection. These findings highlight the effectiveness of combining synthetic and real data to reduce annotation effort while supporting robust tool detection for affordance characterization.

Reliable data acquisition and transmission from ocean-deployed buoys are crucial for maritime applications. However, wireless data transmission in such contexts faces significant challenges due to limited buoy battery capacity, harsh weather conditions, and potential disruptions from maritime vessels. LoRaWAN technology presents a promising solution due to its low power consumption and long-range communication capabilities. Multi-hop routing can further enhance network efficiency by enabling data relaying between buoys. However, the standard LoRaWAN framework lacks native support for multi-hop routing. Addressing this limitation, this paper presents a novel multi-hop routing protocol called OSR specifically designed for LoRaWAN mesh networks deployed in maritime environments. The protocol’s effectiveness is evaluated through a simulation model that accurately reflects the detrimental effects of severe weather on data transmission. Key performance metrics, such as packet delivery ratio, end-to-end latency, and energy consumption are analysed. The results underscore the superiority of OSR over the conventional GRP under realistic marine channel conditions.

LoRaWAN technology plays a pivotal role in enabling data transmission from IoT devices across various industries. In the maritime sector, applications such as operational monitoring and environmental surveillance depend critically on reliable data communication. However, wireless data transmission at sea presents significant challenges, including limited device battery life, harsh weather conditions, and interference from vessels. These challenges necessitate robust communication solutions that exceed the capabilities of the standard LoRaWanprotocol, which lacks native multi-hop routing functionalities. This paper proposes LoRaCAPS, a novel LoRaWAN protocol specifically designed to address these maritime communication challenges. LoRaCAPS integrates multi-hop routing capabilities to improve network coverage and resilience. Key performance metrics, such as packet delivery ratio, latency, and throughput, are analysed to evaluate the effectiveness of LoRaCAPS compared to the conventional geographic routing protocols.

Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.

The last industrial revolutions and the digital transformation have led to a rise of robotics and to the emergence of the concept of digital twin. A major challenge falls within the update of this virtual representation, so that the supervision operator and the system itself can take appropriate decisions. One way to achieve that is to take advantage of the multi-robot perception capabilities by merging their individual observations to collectively enhance object recognition and robot environmental understanding. Since object recognition strongly depends on the viewing angles, one challenge deals with identifying the most relevant camera poses containing the most relevant information about the nature of the object. In this paper we propose a smart view selection approach which aims at determining the poses of the cameras and the number of the most informative views while maximising the object recognition. Based on a synthetic view dataset of traditional industrial objects, we adopt a clustering-based approach for maximising the inter-class distance and minimising the intra-class one. To do so, we compute a score for each view based on the Fowlkes-Mallows Index. This leads us to order the dataset and select a subset of views maximising the score. Then, this subset is used as a training dataset for a knn-classifier. The results, presented in terms of F1-score metric, are promising and highlight the relevance of our work: i) our smart selection enables the collection of a limited number of the most informative camera poses for object recognition; ii) feature extraction from a pre-trained CNN combined with a clustering algorithm allows the separability of industrial object categories; iii) our approach is robust since it provides good performances while the camera poses are in the neighbourhood of the exact camera positions provided by our processing pipeline.

The idea of cooperation has been introduced to self-driving cars about a decade ago with the aim to reduce the occlusion caused by other users or the scene. More recently, the research efforts turned toward cooperative infrastructure bringing a new kind of the point of view as well as more processing power. This paper lies in this new field providing a survey that addresses the cooperative environment. We provide an overview of the architectures available to create such a system as well as the challenges introduced by the cooperation. Later, we review the main blocks involved in the perception: localization, object detection & tracking, map generation. Each block is reviewed under the prism of cooperation. We also provide a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the cooperative perception as well as a list of related scenarios alongside experimentations. Finally, we list some related datasets before concluding our paper, underlining the perspectives for further works.

Despite the utility and benefits of omnidirectional images in robotics and automotive applications, there are no datasets of omnidirectional images available with semantic segmentation, depth map, and dynamic properties. This is due to the time cost and human effort required to annotate ground truth images. This paper presents a framework for generating omnidirectional images using images that are acquired from a virtual environment. For this purpose, we demonstrate the relevance of the proposed framework on two well-known simulators: CARLA Simulator, which is an open-source simulator for autonomous driving research, and Grand Theft Auto V (GTA V), which is a very high quality video game. We explain in details the generated OmniScape dataset, which includes stereo fisheye and catadioptric images acquired from the two front sides of a motorcycle, including semantic segmentation, depth map, intrinsic parameters of the cameras and the dynamic parameters of the motorcycle. It is worth noting that the case of two-wheeled vehicles is more challenging than cars due to the specific dynamic of these vehicles.

This paper presents an industrial staircase localization algorithm based on RGBD data from a tracked robot. This situation is really challenging as the camera is placed close to the ground. Moreover, RGBD can be really noisy on sparse staircases. Contrary to existing works, our evaluation relies on ground truth data provided by a motion capture system. Our experiments suggest that our algorithm can robustly locate industrial staircase. We also propose a new framework to evaluate stair localization performance from RGBD data. The overall performance allows to safety control a robot to rally the staircase.

Visual surveillance of dynamic objects, particularly vehicles on the road, has been, over the past decade, an active research topic in computer vision and intelligent transportation systems communities. In the context of traffic monitoring, important advances have been achieved in environment modeling, vehicle detection, tracking, and behavior analysis. This paper is a survey that addresses particularly the issues related to vehicle monitoring with cameras at road intersections. In fact, the latter has variable architectures and represents a critical area in traffic. Accidents at intersections are extremely dangerous, and most of them are caused by drivers' errors. Several projects have been carried out to enhance the safety of drivers in the special context of intersections. In this paper, we provide an overview of vehicle perception systems at road intersections and representative related data sets. The reader is then given an introductory overview of general vision-based vehicle monitoring approaches. Subsequently and above all, we present a review of studies related to vehicle detection and tracking in intersection-like scenarios. Regarding intersection monitoring, we distinguish and compare roadside (pole-mounted, stationary) and in-vehicle (mobile platforms) systems. Then, we focus on camera-based roadside monitoring systems, with special attention to omnidirectional setups. Finally, we present possible research directions that are likely to improve the performance of vehicle detection and tracking at intersections.

In this paper, we propose a car positioning approach that does not rely on GPS. We propose to use car wheel speeds and road maps in order to achieve robust positioning of the vehicle. The vehicle positioning is achieved by applying particle filtering on a graph-based representation of a road map. We show that the vehicle positioning is feasible and robust with these two inputs at a really low computational cost. We achieve car positioning with an averaged 5 m accuracy within a 100 km drivable road map on a 12 km sequence.

This paper introduces a new framework to improve the performance of Viola and Jones face detector on omnidirectional unwrapped images. First, an optimization scheme is used to improve the unwrapped image specifically for rectangular Haar-like features. Then, we compare our unwrapping approach to the performance obtained with spherical unwrapping. The impact of the decision boundary and candidate window density are also investigated. Our work suggests that our new unwrapping technique improves significantly the performance of Viola and Jones detector on omnidirectional unwrapped images.

In this paper, we tackle the problem of map estimation from small set of vehicular GPS traces collected from low cost devices. Contrary to the existing works, we rely only on GPS information. First, we propose a fast implementation of Kalman filtering of spline-based road modeling. Our approach demonstrates a significant boost of the computation speed while maintained a good estimation error. Secondly, we perform an evaluation of our algorithm on real world data. Our estimation is compared to a high grade Inertial Navigation System and vectorial data gathered from major map providers. Our results suggest that a good performance can be achieved from the fusion of multiple GPS traces collected from multiple vehicles and drivers.

Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline.