用语义地图预测引导机器人高效探索未知区域
SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas
- 通过迭代预测缺失区域，动态规划探索路径
- 在相同时间内覆盖更广地图，精度显著提升
- 适合需要快速构建精准语义地图的机器人任务
本文提出SEA方法，通过语义地图预测与基于强化学习的分层探索策略，实现机器人主动探索。不同于依赖单步目标点预测的现有方法，SEA采用迭代预测-探索框架，基于当前观测显式预测地图缺失区域，并利用实际累积地图与预测全局地图的差异指导探索。同时设计新型奖励机制，借助强化学习优化长期探索策略，在有限步数内构建高精度语义地图。实验表明，该方法在相同时间约束下显著优于现有最先进探索策略，实现更优的地图覆盖率。
原文摘要 · Abstract (English)
In this paper, we propose SEA, a novel approach for active robot exploration through semantic map prediction and a reinforcement learning-based hierarchical exploration policy. Unlike existing learning-based methods that rely on one-step waypoint prediction, our approach enhances the agent's long-term environmental understanding to facilitate more efficient exploration. We propose an iterative prediction-exploration framework that explicitly predicts the missing areas of the map based on current observations. The difference between the actual accumulated map and the predicted global map is then used to guide exploration. Additionally, we design a novel reward mechanism that leverages reinforcement learning to update the long-term exploration strategies, enabling us to construct an accurate semantic map within limited steps. Experimental results demonstrate that our method significantly outperforms state-of-the-art exploration strategies, achieving superior coverage ares of the global map within the same time constraints.
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