In recent decades, modern developed societies have experienced a significant increase in life expectancy, resulting in a significant degree of aging in the population. This demographic shift has had a significant impact on the daily needs and habits of the population. According to recent demographic projections, the elderly population is expected to triple and reach 2 billion people worldwide by 2050. However, future societies around the world are not adequately prepared to face the potential challenges arising from population aging. Elderly people tend to spend more time at home, but the functionalities and ergonomics of current home equipment and appliances do not fully satisfy their needs. Moreover, the absence of familiarity with new, smarter, and automated domestic technologies creates a significant gap that affects the acceptance and adoption of these technologies by the elderly. In this work, we present the most important technical challenges for the integration of assisted living technologies into a Mobile Robotic Platform (MRP). Additionally, we present the ASPiDA concept, a project proposing a holistic system to support elderly people in their domicile environment.

Navigating in a perpetually changing world can provide the basis for numerous challenging autonomous robotic applications. With a view to long-term autonomy, visual place recognition (vPR) systems should be able to robustly operate under extreme appearance changes in their environment. Typically, the utilized data representations are heavily influenced by those changes, negatively affecting the vPR performance. In this article, we propose a sequence-based technique that decouples such changes from the similarity estimation procedure. This is achieved by remapping the sequential representation data into the distance-space domain, i.e., a domain in which we solely consider the distances between image instances, and subsequently normalize them. In such a way, perturbations related to different environmental conditions and embedded into the original representation vectors are avoided, therefore the scene recognition efficacy is enhanced. We evaluate our framework under multiple different instances, with results indicating a significant performance improvement over other approaches.

The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as $Grypho{n}_{urban}$ urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments.

The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety.

Visual Place Recognition (vPR) plays a crucial role in Unmanned Aerial Vehicle (UAV) navigation, enabling robust localization across diverse environments. Despite significant advancements, aerial vPR faces unique challenges due to the limited availability of large-scale, high-altitude datasets, which limits model generalization, along with the inherent rotational ambiguity in UAV imagery. To address these challenges, we introduce LASED, a large-scale aerial dataset with approximately one million images, systematically sampled from 170,000 unique locations throughout Estonia over a decade, offering extensive geographic and temporal diversity. Its structured design ensures clear place separation significantly enhancing model training for aerial scenarios. Furthermore, we propose the integration of steerable Convolutional Neural Networks (CNNs) to explicitly handle rotational variance, leveraging their inherent rotational equivariance to produce robust, orientation-invariant feature representations. Our extensive benchmarking demonstrates that models trained on LASED achieve significantly higher recall compared to those trained on smaller, less diverse datasets, highlighting the benefits of extensive geographic coverage and temporal diversity. Moreover, steerable CNNs effectively address rotational ambiguity inherent in aerial imagery, consistently outperforming conventional convolutional architectures, achieving on average 12\% recall improvement over the best-performing non-steerable network. By combining structured, large-scale datasets with rotation-equivariant neural networks, our approach significantly enhances model robustness and generalization for aerial vPR.