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In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.

The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.

Event-based (neuromorphic) cameras depart from frame-based sensing by reporting asynchronous per-pixel brightness changes. This produces sparse, low-latency data streams with extreme temporal resolution but demands new processing paradigms. In this survey, we systematically examine neuromorphic vision along three main dimensions. First, we highlight the technological evolution and distinctive hardware features of neuromorphic cameras from their inception to recent models. Second, we review image-processing algorithms developed explicitly for event-based data, covering works on feature detection, tracking, optical flow, depth and pose estimation, and object recognition. These techniques, drawn from classical computer vision and modern data-driven approaches, illustrate the breadth of applications enabled by event-based cameras. Third, we present practical application case studies demonstrating how event cameras have been successfully used across various scenarios. Distinct from prior reviews, our survey provides a broader overview by uniquely integrating hardware developments, algorithmic progressions, and real-world applications into a structured, cohesive framework. This explicitly addresses the needs of researchers entering the field or those requiring a balanced synthesis of foundational and recent advancements, without overly specializing in niche areas. Finally, we analyze the challenges limiting widespread adoption, identify research gaps compared to standard imaging techniques, and outline promising directions for future developments.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.

This study presents a novel framework that integrates the universal jamming gripper (UG) with unmanned aerial vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, increasing the level of automation and operational reliability in aerial grasping. This control structure, while simple, is a powerful tool for various applications, ranging from material handling to disaster response, and marks an advancement toward genuine autonomy in aerial manipulation tasks. The key contribution of this research is the combination of a UG with a suitable control strategy, that can be kept relatively straightforward thanks to the mechanical intelligence built into the UG. The algorithm is validated through numerical simulations and virtual experiments.

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, which rely on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale drift, and degenerate motions. In addition, concerning supervised learning, we can fully leverage video stream data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the translational component. Therefore, we present RAUM-VO, an approach based on a model-free epipolar constraint for frame-to-frame motion estimation (F2F) to adjust the rotation during training and online inference. To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol. Then, we adjust the predicted rotation with the motion estimated by F2F using the 2D matches and initializing the solver with the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy improvement compared to other unsupervised pose networks on the KITTI dataset, while reducing the complexity of other hybrid or traditional approaches and achieving comparable state-of-the-art results.

Space exploration demands innovative robotic solutions to address complex challenges. This article provides a forward‐looking perspective on the emerging field of compliant robotics for space applications, categorizing these systems into reconfigurable, hyper‐redundant, origami‐inspired, and soft robots, each offering unique advantages and facing distinct challenges. The review explores in‐depth the critical roles these compliant robots can assume, ranging from on‐orbit servicing to planetary exploration and beyond. It also addresses material selection, accounting for the harsh conditions of space, and examines the complexities in design, actuation, sensing, and control. The article concludes with a future‐focused discussion of emerging trends, challenges, and research directions. This review aims to offer a comprehensive understanding of the current state of the art, positioning compliant robotics as a transformative force in the next frontier of space exploration. Compliant robots are increasingly becoming integral to space exploration due to their adaptability, flexibility, and lightweight design. This article reviews categories such as soft, reconfigurable, and hyper‐redundant robots and their evolving role in enhancing space missions. With advancements in autonomy and materials, these robots are expected to address challenges posed by long‐duration missions and extreme space environments.

Deliberative capabilities are essential for intelligent aerial robotic applications in modern life such as package delivery and surveillance. This paper presents a real-time 3D path planning solution for multirotor aerial robots to obtain a feasible, optimal and collision-free path in complex dynamic environments. High-level geometric primitives are employed to compactly represent the situation, which includes self-situation of the robot and situation of the obstacles in the environment. A probabilistic graph is utilized to sample the admissible space without taking into account the existing obstacles. Whenever a planning query is received, the generated probabilistic graph is then explored by an A ⋆ discrete search algorithm with an artificial field map as cost function in order to obtain a raw optimal collision-free path, which is subsequently shortened. Realistic simulations in V-REP simulator have been created to validate the proposed path planning solution, integrating it into a fully autonomous multirotor aerial robotic system.