用地图预测提升机器人在杂乱环境中的探索效率
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
- 基于预测地图规划房间访问顺序，实现高层级探索引导
- 在仿真中使路径长度减少2.18%至34.60%
- 适合需高效探索未知复杂环境的机器人系统
机器人探索旨在重建未知环境，关键在于缩短路径。传统方法仅依据当前观测优化边界点访问顺序，易陷入局部最优。近期通过预测未见环境结构可提升效率，但在障碍随机分布的杂乱环境中，预测能力受限，导致改进有限。为此，我们提出FPUNet，能高效预测噪声室内环境布局；进而提取房间分割并构建其拓扑连接关系，优化预测房间的访问顺序，为探索提供高层指导。FPUNet相较于其他网络架构表现更优。大量仿真实验表明，该方法相比基线可使路径长度减少2.18%至34.60%。
原文摘要 · Abstract (English)
Robot exploration aims at the reconstruction of unknown environments, and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting order of frontiers based on current observations, which may lead to local-minimal results. Recently, by predicting the structure of the unseen environment, the exploration efficiency can be further improved. However, in a cluttered environment, due to the randomness of obstacles, the ability to predict is weak. Moreover, this inaccuracy will lead to limited improvement in exploration. Therefore, we propose FPUNet which can be efficient in predicting the layout of noisy indoor environments. Then, we extract the segmentation of rooms and construct their topological connectivity based on the predicted map. The visiting order of these predicted rooms is optimized which can provide high-level guidance for exploration. The FPUNet is compared with other network architectures which demonstrates it is the SOTA method for this task. Extensive experiments in simulations show that our method can shorten the path length by 2.18% to 34.60% compared to the baselines.
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