Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

Motivated by the need to regulate and regularly inspect construction sites, we developed a real-time robust drone system that performs site inspection and violation detection in construction sites. The proposed system uses the drone to inspect and monitor numerous types of construction sites remotely from any control station or a nearby locations. Our drone system was developed as an entry to participate in “Drones For Good” 2015 competition, and qualified to the finals. Our proposed technological solution presented in this paper introduces the possible utilization of drone technology in the construction industry. We will first introduce the motivation and benefits of using this system, and how it's usage will increase the ability to control the health and safety, efficiency, and cost of the site inspection process. We will then detail the technical components of the drone, and present the results of experiments and evaluations conducted in realistic scenarios.

In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the past two decades as a powerful tool, empowering robots with the ability to navigate and map their surroundings. While these methods are traditionally confined to static environments, there has been a growing interest in developing V-SLAM to handle dynamic and realistic scenes. This survey offers a comprehensive overview of the current state-of-the-art V-SLAM methods, including their strengths and weaknesses. The paper also identifies the limitations of existing techniques and proposes potential research directions for future advancements. In addition, it provides an overview of commonly used datasets to evaluate the performance of V-SLAM methods. This survey sheds valuable insights into areas that need additional research to benefit V-SLAM development, including challenges related to limited scalability for systems with multiple agents, sensitivity to lighting changes, high computational cost, and performance issues in noisy environments.

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. Several navigational techniques have been successfully developed in the past to assist with specific behaviours such as "door passing" or "corridor following". These shared control strategies normally require the user to manually select the level of assistance required during use. Recent research has seen a move towards more intelligent systems that focus on forecasting users' intentions based on current and past actions. However, these predictions have been typically limited to locations immediately surrounding the wheelchair. The key contribution of the work presented here is the ability to predict the users' intended destination at a larger scale, that of a typical office arena. The systems relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently drive the user to his destination. The projection is constantly being updated, allowing for true user- platform integration. This shifts users' focus from fine motor- skilled control to coarse control broadly intended to convey intention. Successful simulation and experimental results on a real wheelchair robot demonstrate the validity of the approach.