Refine
Document Type
- Conference Proceeding (22)
- Article (reviewed) (5)
Conference Type
- Konferenzartikel (22)
Is part of the Bibliography
- yes (27)
Keywords
- Kalman Filter (2)
- Air Pollution (1)
- Deep Learning (1)
- EKF-SLAM (1)
- Entfernung (1)
- Environmental monitoring (1)
- Geschwindigkeit (1)
- Inertial (1)
- LOAM (LiDAR odometry and mapping) (1)
- LiDAR (1)
Institute
Open Access
- Closed Access (12)
- Open Access (10)
- Closed (3)
- Bronze (2)
- Gold (2)
A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.