Fachbereich 4
Filtern
Erscheinungsjahr
Dokumenttyp
- Ausgabe (Heft) zu einer Zeitschrift (28)
- Bachelorarbeit (2)
- Dissertation (2)
- Masterarbeit (2)
- Wissenschaftlicher Artikel (1)
Schlagworte
- Artificial Intelligence (1)
- Colonoscopy (1)
- Darmspiegelung (1)
- Datenschutz, Datensicherheit, Apps, Informatik im Kontext (1)
- ERP-Systeme (1)
- Enterprise-Resource-Planning (1)
- Evaluation (1)
- Graph Technology (1)
- IEC 61131 (1)
- IT security analysis (1)
- Koloskopie (1)
- Mixed method (1)
- Online-Überwachung (1)
- Open Source (1)
- OpenVDB (1)
- Polypendetektion (1)
- Programmiersprachen (1)
- RMIS (1)
- ReDSeeDS-Project (1)
- Real-Time (1)
- Risikomanagement-Informationssysteme (1)
- Router (1)
- Routing Information Protocol (RIP) (1)
- Routing Loops (1)
- Routing with Metric based Topology Investigation (RMTI) (1)
- Schutzprofil (1)
- Semantik (1)
- Software Development (1)
- Syntax (1)
- Traceability (1)
- United Internet AG (1)
- activation functions of neurons (1)
- artifcial neural networks (1)
- computer clusters (1)
- constraint logic programming (1)
- delivery drone (1)
- deutschsprachiger Markt (1)
- drone (1)
- event model (1)
- event-based systems (1)
- hybrid systems (1)
- media competence model (1)
- mobile phones (1)
- ontology (1)
- parallel algorithms (1)
- personal information management (1)
- persönliches Informationsmanagement (1)
- polyp detection (1)
- privacy and personal data (1)
- privacy competence model (1)
- privacy protection (1)
- risk (1)
- risks (1)
- security awareness (1)
- semantic desktop (1)
- semantischer Desktop (1)
- sensor data (1)
- summative evaluation (1)
- technology acceptance model (1)
- traffic survey (1)
Institut
- Fachbereich 4 (35) (entfernen)
This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.