TY - CHAP U1 - Konferenzveröffentlichung A1 - Weber, Thomas A1 - Triputen, Sergey A1 - Gopal, Atmaraaj A1 - Eißler, Steffen A1 - Höfert, Christian A1 - Schreve, Kristiaan A1 - Rätsch, Matthias ED - Chalup, Stephan T1 - Efficient and robust 3D object reconstruction based on monocular SLAM and CNN semantic segmentation T2 - RoboCup 2019: Robot World Cup XXIII. - (Lecture notes in artificial intelligence ; 11531) N2 - Various applications implement slam technology, especially in the field of robot navigation. We show the advantage of slam technology for independent 3d object reconstruction. To receive a point cloud of every object of interest void of its environment, we leverage deep learning. We utilize recent cnn deep learning research for accurate semantic segmentation of objects. In this work, we propose two fusion methods for cnn-based semantic segmentation and slam for the 3d reconstruction of objects of interest in order to obtain a more robustness and efficiency. As a major novelty, we introduce a cnn-based masking to focus slam only on feature points belonging to every single object. Noisy, complex or even non-rigid features in the background are filtered out, improving the estimation of the camera pose and the 3d point cloud of each object. Our experiments are constrained to the reconstruction of industrial objects. We present an analysis of the accuracy and performance of each method and compare the two methods describing their pros and cons. KW - 3d reconstruction KW - slam KW - lsd-slam KW - monocular camera KW - cnn KW - semantic segmentation KW - bin-picking KW - collaborative robot KW - depth estimation Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-35699-6_27 DO - https://doi.org/10.1007/978-3-030-35699-6_27 SP - 351 EP - 363 S1 - 13 PB - Springer CY - Cham ER -