S-MKI: Incremental Dense Semantic Occupancy Reconstruction Through Multi-Entropy Kernel Inference

Autonomous robots are often required to acquire high-level prior knowledge by continuously reconstructing the semantics and geometry of the surrounding scene, which is the basis of exploration and planning. Most existing continuous semantic mapping algorithms cannot distinguish potential differences in voxels, resulting in an over-inflated map. Furthermore, fixed-size query ranges introduce high computational complexity. Based on the limitation of over-inflation and inefficiency, this paper proposes a novel incremental continuous semantic occupancy mapping algorithm (S-MKI). The key innovation of this work comes from the two models in the preprocessing stage. On the one hand, Redundant Voxel Filter Model utilizes context entropy to filter out redundant voxels to improve the confidence of the final map, where objects have accurate boundaries with sharp edges. On the other hand, Adaptive Kernel Length Model adaptively adjusts the kernel length with class entropy, which reduces the inherent amount of training data. The final multientropy kernel inference function is formulated to integrate these two models to infer sparse noisy sensor data into dense accurate 3D maps. Experimental results conducted in both indoors and outdoors datasets validate that S-MKI outperforms existing methods.