He teaches a number of undergraduate and graduate courses in the field of control systems and mobile robotics. Since 1994 he has been with FER Zagreb, where he is currently a full professor. He had been employed as an R&D Engineer at the Institute of Electrical Engineering of the Konar Corporation in Zagreb from 1985 to 1994. degree in 1998, all in Electrical Engineering from the Faculty of Electrical Engineering and Computing (FER Zagreb), University of Zagreb, Croatia.
The indoor experiment was conducted using a research mobile robot Husky A200 to map our university building and the outdoor experiment was performed on the publicly available dataset provided by the Ford Motor Company, in which a car equipped with a 3D LIDAR was driven in the downtown Dearborn Michigan. Complete SLAM system was also tested in one indoor and one outdoor experiment. The proposed point cloud segmentation and registration method was tested and compared with the several state-of-the-art methods on two publicly available datasets. Finally, our SLAM system enables reconstruction of the global map by merging the local planar surface segments in a highly efficient way. The SLAM backend is based on Exactly Sparse Delayed State Filter as a non-iterative way of updating the pose graph and exploiting sparsity of the SLAM information matrix. For efficient point cloud processing we apply image-based techniques to project it to three two-dimensional images.
Full FOV and planar representation of the map gives the proposed SLAM system the capability to map large-scale environments while maintaining fast execution time. In this paper we propose a fast 3D pose based SLAM system that estimates a vehicle’s trajectory by registering sets of planar surface segments, extracted from 36 0 ∘ field of view (FOV) point clouds provided by a 3D LIDAR.
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