Stereo vision, resulting in the knowledge of deep information in a scene, is of great importance in the field of machine vision, robotics and image analysis. In this article, an explicit analysis of the existing stereo matching methods, up to date, is presented. The presented algorithms are discussed in terms of speed, accuracy, coverage, time consumption, and disparity range. Towards the direction of real-time operation, the development of stereo matching algorithms, suitable for efficient hardware implementation is highly desirable. Implementations of stereo matching algorithms in hardware for real-time applications are also discussed in details.

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.

Agriculture 5.0 refers to the next phase of agricultural development, building upon the previous digital revolution in the agrarian sector and aiming to transform the agricultural industry to be smarter, more effective, and ecologically conscious. Farming processes have already started becoming more efficient due to the development of digital technologies, including big data, artificial intelligence (AI), robotics, the Internet of Things (IoT), and virtual and augmented reality. Farmers can make the most of the resources at their disposal thanks to this data-driven approach, allowing them to effectively cultivate and sustain crops on arable land. The European Union (EU) aims to make food systems fair, healthy, and environmentally sustainable through the Green Deal and its farm-to-fork, soil, and biodiversity strategies, zero pollution action plan, and upcoming sustainable use of pesticides regulation. Many of the historical synthetic pesticides are not currently registered in the EU market. In addition, the continuous use of a limited number of active ingredients with the same mode of action scales up pests/pathogens/weed resistance potential. Increasing plant protection challenges as well as having fewer chemical pesticides to apply require innovation and smart solutions for crop production. Biopesticides tend to pose fewer risks to human health and the environment, their efficacy depends on various factors that cannot be controlled through traditional application strategies. This paper aims to disclose the contribution of robotic systems in Agriculture 5.0 ecosystems, highlighting both the challenges and limitations of this technology. Specifically, this work documents current threats to agriculture (climate change, invasive pests, diseases, and costs) and how robotics and AI can act as countermeasures to deal with such threats. Finally, specific case studies and the application of intelligent robotic systems to them are analyzed, and the architecture for our intelligent decision system is proposed.