Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new localization algorithm that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the system's output. As novelties, our work includes a multi-scale map-matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state of the art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive AMCL solution, obtaining good accuracy/recovery times.

Motion Capture systems are crucial in many fields, and Mobile Robotics is one of them. This paper describes an Open Source robotic framework to standardize the use of motion capture systems called MOCAP4ROS2. This framework features a layered architecture that allows building applications that use Motion Capture systems regardless of the specific system model/vendor. The challenges are technical and social: on the one hand, resolving synchronization and representation issues; on the other hand, involving the community to reach a consensus on the necessary interfaces. MOCAP4ROS2 has been implemented in ROS2 and already has drivers (we understand a driver for MOCA4ROS2 as a ROS2 node that publish the MOCAP system information) for today’s main commercial systems.

Abstract Nowadays,the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.