Ville Lehtola
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Simultaneous Localization and Mapping (SLAM) has been foundational for autonomous systems, with GraphSLAM being a prominent variant that optimizes mapping as a graph-based optimization
Calibration of multi-sensor systems is a pivotal step in ensuring the accuracy and reliability of data derived from these sensors.
While Graph-SLAM techniques have significantly contributed to mobile mapping advancements in indoor and urban environments, there remains a need to improve the quality of data gen
Simultaneous Localization and Mapping (SLAM) techniques are critical for mobile mapping in indoor and urban environments.
Simultaneous Localization and Mapping (SLAM) techniques, especially graph-SLAM techniques, can be used for mob
Simultaneous Localization and Mapping (SLAM) techniques, especially Extended Kalman Filtering techniques, can
3D models such as Building information models (BIM) are valuable for industry for various planning purposes.
Data acquired from indoor environments contains a lot of valuable information about the assets and facilities.
3D models such as Building information models (BIM) are valuable for industry for various planning purposes.
Detection and classification of objects with machine/deep learning for asset management in buildings
Asset management is important for commercial and public buildings.