We extended the well known Kinect Fusion approach [11] with a particle filter framework to improve the tracking of abrupt camera movements, while the estimated camera pose is further refined with the ICP algorithm. All performance-critical algorithms were implemented on modern graphics hardware using the CUDA GPGPU language and are largely parallelized. It has been shown that our procedure has only minimal reduced precision compared to known techniques, but provides higher robustness against abrupt camera movements and dynamic occlusions. Furthermore the algorithm runs at a frame-time of approx. 24.6098 ms on modern hardware, hence enabling real time capability.
CITATION STYLE
Höhner, N., Hebborn, A. K., & Müller, S. (2018). Particle filter based tracking and mapping. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11241 LNCS, pp. 299–308). Springer Verlag. https://doi.org/10.1007/978-3-030-03801-4_27
Mendeley helps you to discover research relevant for your work.