Title: Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation,
Abstract: We propose an efficient and scalable method for incrementally building a
dense, semantically annotated 3D map in real-time. The proposed method assigns
class probabilities to each region, not each element (e.g., surfel and voxel),
of the 3D map which is built up through a robust SLAM framework and
incrementally segmented with a geometric-based segmentation method. Differently
from all other approaches, our method has a capability of running at over 30Hz
while performing all processing components, including SLAM, segmentation, 2D
recognition, and updating class probabilities of each segmentation label at
every incoming frame, thanks to the high efficiency that characterizes the
computationally intensive stages of our framework. By utilizing a specifically
designed CNN to improve the frame-wise segmentation result, we can also achieve
high accuracy. We validate our method on the NYUv2 dataset by comparing with
the state of the art in terms of accuracy and computational efficiency, and by
means of an analysis in terms of time and space complexity.