%0 Conference Paper %1 ICCCR2021 %A Du, S. %A Li, X. %A Lauterbach, H. A. %A Borrmann, D. %A Nüchter, A. %B Proceedings of the IEEE International Conference on Computer, Control and Robotics (ICCCR '21) %D 2021 %K imported myown %P 335--339 %R 10.1109/ICCCR49711.2021.9349385 %T Combining LiDAR Scan Matching with Stereo Visual Odometry Using Curvefusion %U https://robotik.informatik.uni-wuerzburg.de/telematics/download/icccr2021.pdf %X In this paper, we present a novel algorithm, namely Curvefusion for integrating LiDAR scan matching with stereo visual odometry. First, 6-DOF pose trajectories are estimated by utilizing SOFT odometry, which is the state of the art stereo visual odometry based on feature selection and tracking, and the well-known ICP scan matching algorithm, respectively. Second, a deformation-based multi-sensor fusion method, namely curvefusion is applied. The proposed fusion method does not rely on a sensor model. As long as the trajectories of the sensors to be fused are given, we can obtain an optimized fusion trajectory, which greatly improves the computational efficiency. Experiments based on publicly available KITTI data set show that the proposed method outperforms or achieves similar performance compared with the state-of-the-art odometry methods.

@inproceedings{ICCCR2021, abstract = {In this paper, we present a novel algorithm, namely Curvefusion for integrating LiDAR scan matching with stereo visual odometry. First, 6-DOF pose trajectories are estimated by utilizing SOFT odometry, which is the state of the art stereo visual odometry based on feature selection and tracking, and the well-known ICP scan matching algorithm, respectively. Second, a deformation-based multi-sensor fusion method, namely curvefusion is applied. The proposed fusion method does not rely on a sensor model. As long as the trajectories of the sensors to be fused are given, we can obtain an optimized fusion trajectory, which greatly improves the computational efficiency. Experiments based on publicly available KITTI data set show that the proposed method outperforms or achieves similar performance compared with the state-of-the-art odometry methods.}, added-at = {2021-03-14T14:35:44.000+0100}, author = {{Du}, S. and {Li}, X. and {Lauterbach}, H. A. and {Borrmann}, D. and {N{\"u}chter}, A.}, biburl = {https://www.bibsonomy.org/bibtex/2430d6e3bdd4e487e5cfd0ac4e861dc52/nuechter76}, booktitle = {Proceedings of the IEEE International Conference on Computer, Control and Robotics (ICCCR '21)}, doi = {10.1109/ICCCR49711.2021.9349385}, interhash = {71ba358f06b58ca7d5682aca4ef18f44}, intrahash = {430d6e3bdd4e487e5cfd0ac4e861dc52}, keywords = {imported myown}, pages = {335--339}, timestamp = {2021-06-08T10:44:42.000+0200}, title = {Combining LiDAR Scan Matching with Stereo Visual Odometry Using Curvefusion}, url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/icccr2021.pdf}, year = 2021 }