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Last-mile delivery systems commonly propose the use of autonomous roboticvehicles to increase scalability and efficiency. The economic inefficiency ofcollecting accurate prior maps for navigation motivates the use of planningalgorithms that operate in unmapped environments. However, these algorithmstypically waste time exploring regions that are unlikely to contain thedelivery destination. Context is key information about structured environmentsthat could guide exploration toward the unknown goal location, but the abstractidea is difficult to quantify for use in a planning algorithm. Some approachesspecifically consider contextual relationships between objects, but wouldperform poorly in object-sparse environments like outdoors. Recent deeplearning-based approaches consider context too generally, makingtraining/transferability difficult. Therefore, this work proposes a novelformulation of utilizing context for planning as an image-to-image translationproblem, which is shown to extract terrain context from semantic gridmaps, intoa metric that an exploration-based planner can use. The proposed framework hasthe benefit of training on a static dataset instead of requiring atime-consuming simulator. Across 42 test houses with layouts from satelliteimages, the trained algorithm enables a robot to reach its goal 189\% fasterthan with a context-unaware planner, and within 63\% of the optimal pathcomputed with a prior map. The proposed algorithm is also implemented on avehicle with a forward-facing camera in a high-fidelity, Unreal simulation ofneighborhood houses.

Planning Beyond the Sensing Horizon Using a Learned Context