Abstract
The expansion of autonomous driving operations requires the research and development of accurate and reliable self-localization approaches. These include visual odometry methods, in which accuracy is potentially superior to GNSS-based techniques while also working in signal-denied areas. This paper presents an in-depth review of state-of-the-art visual and point cloud odometry methods, along with a direct performance comparison of some of these techniques in the autonomous driving context. The evaluated methods include camera, LiDAR, and multi-modal approaches, featuring knowledge and learning-based algorithms, which are compared from a common perspective. This set is subject to a series of tests on road driving public datasets, from which the performance of these techniques is benchmarked and quantitatively measured. Furthermore, we closely discuss their effectiveness against challenging conditions such as pronounced lighting variations, open spaces, and the presence of dynamic objects in the scene. The research demonstrates increased accuracy in point cloud-based methods by surpassing visual techniques by roughly 33.14% in trajectory error. This survey also identifies a performance stagnation in state-of-the-art methodologies, especially in complex conditions. We also examine how multi-modal architectures can circumvent individual sensor limitations. This aligns with the benchmarking results, where the multi-modal algorithms exhibit greater consistency across all scenarios, outperforming the best LiDAR method (CT-ICP) by 5.68% in translational drift. Additionally, we address how current AI advances constitute a way to overcome the current development plateau.

Abstract
Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Con-volutional Neural Network trained through transfer learning, a deep learning tech-nique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixoes and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light De-tection And Ranging, GPS, and Inertial Measurement Unit data. Images were ex-tracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient informa-tion for simple navigation tasks.

Abstract
Underwater autonomous manipulation is the capability of a mobile robot to perform intervention tasks that require physical contact with unstructured environments without continuous human supervision. Being difficult to assess the behaviour of existing motion planner algorithms, this research proposes a new planner evaluation metric to identify well-behaved planners for specialized tasks of inspection and monitoring of man-made underwater structures. This metric is named NEMU and combines three different performance indicators: effectiveness, safety and adaptability. NEMU deals with the randomization of sampling-based motion planners. Moreover, this article presents a benchmark of multiple planners applied to a 6 DoF manipulator operating underwater. Results conducted in real scenarios show that different planners are better suited for different tasks. Experiments demonstrate that the NEMU metric can be used to distinguish the performance of planners for particular movement conditions. Moreover, it identifies the most promising planner for collision-free motion planning, being a valuable contribution for the inspection of maritime structures, as well as for the manipulation procedures of autonomous underwater vehicles during close range operations.

Abstract
For Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAVs) to operate autonomously and effectively, it is mandatory to endow them with precise landing abilities. The UAV has to be able to detect the landing target and to perform the landing maneuver without compromising its own safety and the integrity of its surroundings. However, current UAVs do not present the required robustness and reliability for precise landing in highly demanding scenarios, particularly due to their inadequacy to perform accordingly under challenging lighting and weather conditions, including in day and night operations.This work proposes a multimodal fiducial marker, named ArTuga (Augmented Reality Tag for Unmanned vision-Guided Aircraft), capable of being detected by an heterogeneous perception system for accurate and precise landing in challenging environments and daylight conditions. This research combines photometric and radiometric information by proposing a real-time multimodal fusion technique that ensures a robust and reliable detection of the landing target in severe environments.Experimental results using a real multicopter UAV show that the system was able to detect the proposed marker in adverse conditions (such as at different heights, with intense sunlight and in dark environments). The obtained average accuracy for position estimation at 1 m height was of 0.0060 m with a standard deviation of 0.0003 m. Precise landing tests obtained an average deviation of 0.027 m from the proposed marker, with a standard deviation of 0.026 m. These results demonstrate the relevance of the proposed system for the precise landing in adverse conditions, such as in day and night operations with harsh weather conditions.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract
A perception module is a vital component of a modern robotic system. Vision, radar, thermal, and LiDAR are the most common choices of sensors for environmental awareness. Relying on singular sources of information is prone to be affected by specific environmental conditions (e.g., visual cameras are affected by glary or dark environments). Thus, relying on different sensors is an essential step to introduce robustness against various environmental conditions. Hence, a perception system with sensor fusion capabilities produces the desired redundant and reliable awareness critical for real-world systems. This paper proposes a novel early fusion module that is reliable against individual cases of sensor failure when detecting an offshore maritime platform for UAV landing. The model explores the early fusion of a still unexplored combination of visual, infrared, and LiDAR modalities. The contribution is described by suggesting a simple methodology that intends to facilitate the training and inference of a lightweight state-of-the-art object detector. The early fusion based detector achieves solid detection recalls up to 99% for all cases of sensor failure and extreme weather conditions such as glary, dark, and foggy scenarios in fair real-time inference duration below 6 ms.

Abstract
The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.