2,699 research outputs found
Ninth International Symposium “Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques” Livorno (Italy) 14th-16th June 2022
No full textReal-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction
No full textIn robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it, and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of graphics processing unit (GPU) computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real time. In this letter, we improved and extended the state-of-the-art incremental manifold mesh algorithm proposed by Litvinov and Lhuillier and extended by Romanoni and Matteucci. While these algorithms do not reconstruct the map in real time and they embed points from SLAM or structure from motion only when their position is fixed, in this letter, we propose the first incremental algorithm able to reconstruct a manifold mesh in real time through single core CPU processing, which is aso able to modify the mesh according to three-dimensional points updates from the underlying SLAM algorithm. We tested our algorithm against two state-of-the-art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up
ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM
No full textReal-time six degrees-of-freedom pose estimation with ground vehicles represents a relevant and well-studied topic in robotics due to its many applications such as autonomous driving and 3D mapping. Although some systems already exist, they are either not accurate or they struggle in real-time settings. In this letter, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking, possibly aided by a pre-tracking module, and floor detection, to ground optimize the estimated trajectory. Efficient multi-steps loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud-based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI (urban driving) and Chilean (underground mine) datasets
Using AADL to model and develop ROS-based robotic application
No full textModern robotic systems are a combination of sophisticated software and hardware components and they offer complex functionalities. While popular middlewares that promote component-level reusability and assist development already exist, there are no established techniques or procedures that use a formal approach to robot system and architecture design yet. This work aims at the long term goal of model-based design and development of complex robot systems (and their software architectures), by surpassing current techniques based on personal expertise, and best practices, in favor of purely model-based approaches. Our contribution tackles the problem from the ground up by proposing a way to model ROS nodes, and robotic architectures in general, using the Architecture Analysis and Design Language (AADL). The result is connected to and based on ROS, but not bound to it. It provides a starting point for the future definition of a general formal framework to describe complex robotic architectures suitable for automatic code generation and system verification
OD Matrices Estimation From Link Flows By Neural Networks And PCA
No full textThe paper tackles OD matrix estimation starting from the measures of flow on road network links and proposes the application of soft-computing techniques. The application scenarios are two: a trial network and the real rural network of the Province of Naples both simulated by a micro-simulator dynamically assigning known OD matrices. A PCA (Principal Component Analysis) technique was also used to reduce the input space of variables in order to achieve better significance for input data and to study the possible eigengraphs of the road networks
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