We present a system that enables robots to interpret spatial language as a distribution over object locations for effective search in partially observable cityscale environments. We introduce the spatial language observation space and formulate a stochastic observation model under the framework of Partially Observable Markov Decision Process (POMDP) which incorporates information extracted from the spatial language into the robot's belief. To interpret ambiguous, context-dependent prepositions (e.g.~front), we propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given environment context. We demonstrate the generalizability of our FoR prediction model and object search system through cross-validation over areas of five cities, each with a 40,000m$^2$ footprint. End-to-end experiments in simulation show that our system achieves faster search and higher success rate compared to a keyword-based baseline without spatial preposition understanding.

提出了 SLOOP 概念，通过使用概率观测模型解决了空间语言的主观性和潜在的模糊性问题，利用在线 POMDP 计划器和 Monte Carlo Tree Search 策略，应用于城市规模环境中的目标搜寻任务，评估表明与基准相比，该方法具有更快的搜寻和更高的成功率，并在 AirSim 中演示了其有效性。

部分可见城市环境中物体搜索的空间语言理解