Abstract
Reliable localization without GPS is a prerequisite for autonomous unmanned aerial vehicles (UAVs) operating inside warehouses, where a lightweight monocular camera paired with an inertial measurement unit (IMU) and onboard computer are the minimal sensing and processing an onboard platform can carry. This paper presents a tightly coupled monocular point-feature visual-inertial odometry (VIO) system for that setting, realized on a GTSAM fixed-lag factor graph with inverse-depth landmarks, on-manifold IMU preintegration, and an online loop-closure pose graph. The system is developed as the initial estimation stage of an autonomous stock-management UAV under development for indoor logistics warehouses. The decisive design element is the bootstrap: the metric, gravity-aligned initialization of a monocular estimator is well conditioned only under a translation-rich trajectory, a condition the near-zero-baseline pickup and takeoff transient that opens every indoor flight violates. Building on the visual-inertial alignment of VINS-Mono, we harden this step with a pre-bundle-adjust conditioning gate and a continuous-window initialization that refines the whole bootstrap window inside the smoother instead of freezing a single seed. On all eleven EuRoC MAV sequences, indoor flight tests recorded onboard a micro air vehicle in an industrial hall and two instrumented rooms, one fixed configuration per operating environment converges on every sequence, including three that otherwise diverge by tens to thousands of meters, and, driven by the same feature stream as locally run VINS-Mono and PL-VINS baselines, attains the better pure-odometry accuracy on nine of the eleven, with ATE RMSE of 0.12–0.37 m on the Machine Hall, a margin a paired signed-rank test confirms against VINS-Mono and leaves unconfirmed against PL-VINS at this sample size. We identify the stock fixed-lag marginalization as the principal consistency limitation and outline First-Estimates-Jacobian marginalization as the route to a more consistent estimator, establishing a characterized point-only baseline on one public benchmark as the starting point for subsequent on-platform work.

Abstract
Accurate dynamic modeling of ground robots (Unmanned Ground Vehicles - UGVs) is essential for robust control and navigation in agricultural environments, where variations in soil friction and rolling resistance significantly affect system dynamics. This work proposes a Linear Parameter-Varying (LPV) model parameterized by the friction coefficient, identified under different soil conditions using two excitation strategies: Amplitude-Pseudo-Random Binary Sequence (APRBS) and standard maneuvers (SM). A simulated ground robot - the Clearpath Husky - was used under multiple soil friction scenarios within the ROS 2 and Gazebo simulation environment. The results show that the LPV model effectively captures the influence of soil friction, with both LPV APRBS and LPV SM yielding similar RMSE values across scenarios. The results also highlight the feasibility of using SM-based excitation for identifying the robot dynamics.

Abstract
This paper covers the state-of-the-art perception and control technologies in precision spraying and mowing in permanent crops. The search was performed in six different databases, resulting in 1849 publications, from which only 94 were considered for inclusion in this review. The analysis highlighted the importance of canopy characteristics in precision spraying, focusing on parameters like height, width, leaf area, and volume, primarily using LiDAR sensors. Vision sensors also complemented LiDAR-based approaches, with diverse applications such as fruit detection and disease diagnosis. Despite valuable knowledge from studies on spray coverage assessment and real-time smartphone analysis, challenges persist, including dynamic environmental factors and the different collector materials used. Moreover, the review considers the cost of Variable Rate Technology (VRT) solutions in agriculture, enhancing their impact on accessibility, adoption, and sustainability. While conventional herbicide-based weed management prevails, interest in alternative techniques like mechanical mowing and organic mulches is growing, promising improved soil health and reduced environmental impact, particularly in permanent crops. To address these challenges, agricultural robotics play a crucial role in automating precision spraying and mowing, optimizing resource usage, and increasing operational precision. This systematic review highlights the state of precision agriculture in permanent crops and emphasizes the need for continued research and development to improve the sustainability and efficiency of precision spraying and mowing systems in orchards, vineyards, and other woody crop environments.

Abstract

Abstract
Automated sound-event detection is crucial for large-scale passive acoustic monitoring of wildlife, but the availability of ready-to-use tools is narrow across taxa. Machine learning is currently the state-of-the-art framework for developing sound-event detection tools tailored to specific wildlife calls. Gray wolves (Canis lupus), a species with intricate management necessities, howl spontaneously for long-distance intra- and inter-pack communication, which makes them a prime target for passive acoustic monitoring. Yet, there is currently no pre-trained, open-access tool that allows reliable automated detection of wolf howls in recorded soundscapes. We collected 50 137 h of soundscape data, where we manually labeled 841 unique howling events. We used this dataset to fine-tune VGGish-a convolutional neural network trained for audio classification-effectively retraining it for wolf howl detection. HOWLish correctly classified 77% of the wolf howling examples present on our test set, with a false positive rate of 1.74%; still, precision was low (0.006) granted extreme class imbalance (7124:1). During field tests, HOWLish retrieved 81.3% of the observed howling events while offering a 15-fold reduction in operator time when compared to fully manual detection. This work establishes the baseline for open-access automated wolf howl detection. HOWLish facilitates remote sensing of wild wolf populations, offering new opportunities in non-invasive large-scale monitoring and communication research of wolves. The knowledge gap we addressed here spans across many soniferous taxa, to which our approach also tallies.

Abstract
Predictive maintenance applications have increasingly been approached with deep learning techniques in recent years due to their high predictive performance. However, as in other real-world application scenarios, the need for explainability is often stated but not sufficiently addressed, which can limit adoption in practice. In this study, we will focus on predicting failures of trains operating in Porto, Portugal. While recent works have found high-performing deep neural network architectures that feature a parallel explainability pipeline, we find that the generated explanations can be hard to comprehend in practice due to their low support over the failure range. In this work, we propose a novel online rule-learning approach that is able to generate simple rules that cover the entirety of the detected failures. We evaluate our method against AMRules, a state-of-the-art online rule-learning approach, on two datasets gathered from trains operated by Metro do Porto. Our experiments show that our approach consistently generates rules with very high support that are simultaneously short and interpretable.