Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status is important for decision makers and citizens alike. A missile is essentially a guided rocket and therefore the term can be used to describe a very wide range of weapon systems. In this paper, we focus on long-range and intercontinental threats, which we believe are more important and problematic to defend against. We provide an overview of the two most common types of sensors, space-based infrared sensors and radars, and highlight their peculiarities and, most importantly, their drawbacks that severely limit their effectiveness.

Developing point clouds registration, SLAM or place recognition algorithms requires data with a high quality ground truth (usually composed of a position and orientation). Moreover, many machine learning algorithms require large amounts of data for training. However, acquiring this kind of data in non-urban outdoor environments poses several challenges. First of all, off-road robots are usually very expensive. Above all, producing an accurate ground truth is problematic. Even the best sensor available, i.e. RTK GPS, cannot guarantee the required accuracy in every condition. Hence the scarcity of this kind of dataset for point clouds registration or SLAM in off-road conditions.

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

Point cloud registration is a vital task in three‐dimensional (3D) perception, with several different applications in robotics. Recent advancements have introduced neural‐based techniques that promise enhanced accuracy and robustness. In this paper, we thoroughly evaluate well‐known neural‐based point cloud registration methods using the Point Clouds Registration Benchmark, which was developed to cover a large variety of use cases. Our evaluation focuses on the performance of these techniques when applied to real‐complex data, which presents a more challenging and realistic scenario than the simpler experiments typically conducted by the original authors. The results reveal considerable variability in performance across different techniques, highlighting the importance of assessing algorithms in realistic settings. Notably, 3DSmoothNet emerges as a standout solution, demonstrating good and consistent results across various data sets. Its efficacy, coupled with a relatively low graphics processing unit (GPU) memory footprint, makes it a promising choice for robotics applications, even if it is not yet suitable for real‐time applications due to its execution time. Fully Convolutional Geometric Features also performs well, albeit with greater variability among data sets. PREDATOR and GeoTransformer are promising, but demand substantial GPU memory, when handling large point clouds from the Point Clouds Registration Benchmark. A notable finding concerns the performance of Fast Point Feature Histograms, which exhibit results comparable to the best approaches while demanding minimal computational resources. Overall, this comparative analysis provides valuable insights into the strengths and limitations of neural‐based registration techniques, both in terms of the quality of the results and the computational resources required. This helps researchers to make informed decisions for robotics applications.