Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
This work addresses the need for continuous and accurate object pose estimates in robotic manipulation, though it is incremental as it extends a previous geometry-based method.
The authors tackled the problem of 6DoF object pose tracking by developing a multi-modality tracker that fuses visual appearance and geometry, resulting in state-of-the-art performance on YCB-Video and OPT datasets with high efficiency at over 300 Hz.
In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learning-based methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available.