Ville Lehtola, Dr. Sc.

LIDAR sensors can yield massive data streams. These data can be reduced to make all following processing easier, by pre-classifying noise and solid (=ground/buildings/tree trunks) and non-solid surfaces (=vegetation), with spatial correlations. These techniques remedy the problem with noisy LIDARs, e.g. single photon LIDARs, which are vulnerable to background illumination (=sunlight). 

Lehtola, Ville V., et al. "Preregistration classification of mobile LIDAR data using spatial correlations." IEEE transactions on geoscience and remote sensing 57.9 (2019): 6900-6915. 

Laser scanning indoor 3D point clouds and capturing imagery are important for reconstructing Building Information Models (BIM). This is a highly cited paper:

Lehtola, Ville V., et al. "Comparison of the selected state-of-the-art 3D indoor scanning and point cloud generation methods." Remote sensing 9.8 (2017): 796. 

Simultaneous Localization And Mapping (SLAM) is the problem of exploring unknown environments with perception systems. SLAM is needed for robotics and mapping in environments where satellite positioning is not available such as indoor, underwater, and forest environments. The 6 degree-of-freedom (DOF) trajectory traversed by the system is shown in white on the image at left. Planar segments are shown in different colors.

Karam, S., Lehtola, V., & Vosselman, G. (2021). Simple loop closing for continuous 6DOF LIDAR&IMU graph SLAM with planar features for indoor environments. ISPRS J of photogrammetry and remote sensing, 181, 413-426.

Digital twin cities (DTC)-technology should serve the city needs. I was city councillor 2009-2017 in Espoo, the most sustainable city of Europe, and combine this knowledge with my technical knowledge and that of my colleagues. DTC founds on BIM and GIS and enables precise planning for economic and ecologic sustainability.

Lehtola, V., Koeva, M., Elberink, S. O., Raposo, P., Virtanen, J. P., Vahdatikhaki, F., & Borsci, S.  (2022). Digital twin of a city: Review of technology serving city needs. International Journal of Applied Earth Observation and Geoinformation, 102915.

Autonomous ships are soon less scifi and more about reality. AI plays a key role. We review techniques how ships can be given super-human eyes and ears. 

Thombre, S., ....,  Lehtola, V. V. (2020). Sensors and AI techniques for situational awareness in autonomous ships: A review. IEEE transactions on intelligent transportation systems, 23(1), 64-83.
https://doi.org/10.1109/TITS.2020.3023957 

E-navigation is essential for autonomous ships. Route planning (here: A* graph search) needs to account for multi-modal data and occurs before the ship leaves port.

Lehtola, V., Montewka, J., Goerlandt, F., Guinness, R., & Lensu, M. (2019). Finding safe and efficient shipping routes in ice-covered waters: A framework and a model. Cold regions science and technology, 165, 102795. 

Autonomy of forest machines requires perception. Here, we use a line scanner and joystick controls to estimate the posture of the crane of a prototype forest machine. Simultaneously, a 3D point cloud of the environment is acquired.

Hyyti, H., Lehtola, V. V., & Visala, A. (2018). Forestry crane posture estimation with a two‐dimensional laser scanner. Journal of Field Robotics, 35(7), 1025-1049. 

Umbra3D Ltd (a NVIDIA spin-off company) creates digital scenes for games and movie industry. We studied the computer vision problem of unocclusion for 3D pointclouds. Update 2021: Umbra3D was sold and fused to another high-tech company.

Väänänen, P., & Lehtola, V. (2019). Inpainting occlusion holes in 3d built environment point clouds. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 393-398. 

3D water vapour distribution can be measured by leveraging low-cost GNSS receiver data! My idea was to combine tropospheric tomography and Precise  Point Positioning (PPP) in a Bayesian scheme. We combined measurements from up to 1000 GNSS receivers, including low-cost sensors. The study was done together with Airbus Defense and Space, in a simulated environment.

Lehtola, V. V., Mäkelä, M., de Oliveira Marques, T., & Montloin, L. (2022). Tropospheric wet tomography and PPP: Joint estimation from GNSS crowdsourcing data. Advances in Space Research, 70(8), 2399-2411.