🤖 AI Summary
Existing semantic mapping approaches struggle to maintain semantic consistency and robustness in dynamic environments due to high computational costs and the neglect of spatiotemporal relationships among voxels. This work proposes a spatiotemporally aware continuous semantic mapping framework that jointly models spatial structure and temporal consistency for the first time. By integrating voxel-level semantic reasoning, local uncertainty–driven adaptive inference ranges, and cross-frame semantic label fusion, the method significantly enhances both mapping efficiency and stability. Evaluated on SemanticKITTI, the approach achieves a mean Intersection over Union (mIoU) of 54.92%, representing a 13.18 percentage point improvement over purely spatial methods, along with approximately a 12% increase in mapping accuracy.
📝 Abstract
Continuous semantic mapping allows autonomous robots to understand both the spatial structure and the semantic content of complex environments. However, most existing methods process the entire space, treat voxels as independent units, and do not keep the semantic labels consistent over time. This leads to high computational cost and reduced robustness in dynamic scenes. This paper proposes a semantic mapping method that brings spatial and temporal relationships into the semantic inference process. The method adjusts the inference range according to the local semantic uncertainty and fuses labels over time to improve map stability and computational efficiency. Experiments on the SemanticKITTI dataset show that the proposed method improves mapping accuracy by about 12% and reaches an mIoU of 54.92%, which is 13.18 percentage points higher than spatial-only mapping. These results show that spatiotemporal reasoning is effective for continuous semantic mapping in autonomous robotic systems.