通过时空关联提升机器人语义地图的准确与稳定
Efficient Continuous Semantic Mapping based on Spatio-Temporal Awareness
- 根据局部语义不确定动态调整推理范围
- 时间融合标签使地图精度提升12%，mIoU达54.92%
- 适合需要长期稳定感知的自主导航系统
连续语义地图使自主机器人能够理解复杂环境的空间结构与语义内容。然而，现有方法通常处理整个空间，将体素视为独立单元，且无法保持语义标签的时间一致性，导致计算开销高，在动态场景中鲁棒性差。本文提出一种将时空关系融入语义推理的映射方法：根据局部语义不确定性自适应调整推理范围，并在时间维度上融合标签以提升地图稳定性与计算效率。在SemanticKITTI数据集上的实验表明，该方法将映射精度提升约12%，达到mIoU 54.92%，较仅依赖空间信息的方法高出13.18个百分点。结果证明，时空推理对自主机器人系统的连续语义映射具有显著有效性。
原文摘要 · Abstract (English)
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.
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