PerSeM：用于长时程开放词汇无人机测绘的持久语义记忆

中文摘要 AI 辅助

开放词汇分割使无人机具备丰富的语义感知能力，但逐帧预测在重复观测和视角变化时可能产生时间上的不一致。我们提出PerSeM，一种无需训练的持久语义记忆框架，用于长时程开放词汇无人机测绘。PerSeM将逐帧语义观测与持久的世界空间体素关联，并构建基于多数的语义记忆，通过保留历史的空间细化、信任感知回放和上下文引导验证进行保守精炼。在Forest和UAVScenes基准上的实验表明，持久3D记忆相比逐帧预测在语义正确性和时间稳定性上带来显著提升。在此强持久记忆基线之上，PerSeM提供了持续额外的改进，在所有五个评估的UAVScenes序列中同时提高了语义准确性和时间稳定性。使用独立于最终PerSeM预测识别的区域进行的分析进一步表明，这些增益集中在语义困难和时间不稳定的区域，这些区域中基于多数的记忆最可能保持不确定。这些结果表明，持久3D聚合为长时程语义测绘提供了坚实基础，而对不确定记忆状态的保守精炼可以在无需重新训练或额外神经网络推理的情况下提供额外改进。

英文摘要

Open-vocabulary segmentation enables rich semantic perception for UAVs, but frame-wise predictions can remain temporally inconsistent across repeated observations and changing viewpoints. We present PerSeM, a training-free persistent semantic memory framework for long-horizon open-vocabulary UAV mapping. PerSeM associates frame-wise semantic observations with persistent world-space voxels and constructs a majority-based semantic memory, which is conservatively refined through history-preserving spatial refinement, trust-aware replay, and context-guided verification. Experiments on the Forest and UAVScenes benchmarks show that persistent 3D memory provides substantial gains in semantic correctness and temporal stability over frame-wise predictions. Beyond this strong persistent-memory baseline, PerSeM provides consistent additional improvements, improving both semantic accuracy and temporal stability across all five evaluated UAVScenes sequences. Analysis using regions identified independently of the final PerSeM predictions further shows that these gains are concentrated in semantically difficult and temporally unstable regions, where majority-based memory is most likely to remain uncertain. These results demonstrate that persistent 3D aggregation provides a strong foundation for long-horizon semantic mapping, while conservative refinement of uncertain memory states can provide additional improvements without retraining or additional neural-network inference.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）

机构由 AI 辅助整理，请以论文原文为准。