Ville Lehtola

GNSS-denied localisation for outdoor robots without GPS

MSc thesis project at ITC-EOS in collaboration with Demcon

The segmentation of multi-line raw LiDAR scans is a challenging and crucial problem in 3D scene understanding.

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.