基于稀疏高斯过程回归的多Agent系统稠密增量度量-语义映射
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression
摘要：
我们开发了一个在线的概率度量语义映射方法的移动机器人团队依赖于流RGB-D观察。所生成的地图包含关于几何表面和语义标签的完全连续的分布信息（例如，椅子、桌子、墙壁）。我们的方法是基于在线高斯过程（GP）的训练和推理，并避免了GP分类的复杂性，通过回归截断符号距离函数（TSDF）的区域所占用的不同的语义类。通过GP后验的稀疏伪点近似实现在线回归。为了扩展到大的环境，我们进一步考虑通过具有重叠叶子的八叉树数据结构进行空间域划分。通过让每个机器人执行其自己的在线测量更新，然后通过局部加权几何平均与其邻居的后验参数相结合，开发了多机器人设置的扩展。这就产生了一个分布式的信息处理架构，其中所有机器人的GP地图估计收敛到一个共同的地图的环境，而只依赖于本地的单跳通信。我们的实验证明了概率度量语义映射技术在2-D和3-D环境中的单机器人和多机器人设置的有效性。
英文摘要：
We develop an online probabilistic metric-semantic mapping approach for mobile robot teams relying on streaming RGB-D observations. The generated maps contain full continuous distributional information about the geometric surfaces and semantic labels (e.g., chair, table, wall). Our approach is based on online Gaussian Process (GP) training and inference, and avoids the complexity of GP classification by regressing a truncated signed distance function (TSDF) of the regions occupied by different semantic classes. Online regression is enabled through a sparse pseudo-point approximation of the GP posterior. To scale to large environments, we further consider spatial domain partitioning via an octree data structure with overlapping leaves. An extension to the multi-robot setting is developed by having each robot execute its own online measurement update and then combine its posterior parameters via local weighted geometric averaging with those of its neighbors. This yields a distributed information processing architecture in which the GP map estimates of all robots converge to a common map of the environment while relying only on local one-hop communication. Our experiments demonstrate the effectiveness of the probabilistic metric-semantic mapping technique in 2-D and 3-D environments in both single and multi-robot settings.
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