HD-CCSOM: Hierarchical and Dense Collaborative Continuous Semantic Occupancy Mapping through Label Diffusion

The collaborative operation of multiple robots can make up for the shortcomings of a single robot, such as limited field of perception or sensor failure. Multi-robots collaborative semantic mapping can enhance their comprehensive contextual understanding of the environment. However, existing multi-robots collaborative semantic mapping algorithms mainly apply discrete occupancy map inference, and do not compensate for inconsistent labels of local maps caused by differences in robot perspectives, which leads to greatly reduced availability and accuracy of the final global map. To address the challenges of discontinuous maps and inconsistent semantic labels, this paper proposes a novel hierarchical and dense collaborative continuous semantic occupancy mapping algorithm (HD-CCSOM). This work decomposes and formulates robot collaborative continuous semantic occupancy mapping problem at two levels. At the single robot level, the multi-entropy kernel inference method smoothly processes the registered semantic point cloud and infers a local continuous semantic occupancy map for each robot. At the collaborative robots level, the local maps are fused into a global enhanced and consistent semantic map via the label diffusion method based on a graph model. The proposed algorithm has been validated on public datasets and in simulated and real scenes, demonstrating significant improvements in mapping accuracy and efficiency.