Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and largescale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.
Finally, in REF , semantic 3D maps were built using a CRF model with higher order.
Semantic 3D occupancy mapping through efficient high order CRFs
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science