Demo: Towards Autonomous Drone Delivery to Your Door Over House-Aware Semantics

Drone delivery is swiftly on the rise. Different from most prior efforts centered on the last-mile problem where the drone flies from the source to the destination house but overlooks the landing part [1, 2]. In our full paper [3], we make the first attempt on the last-hundred-feet problem, which aims to land at a convenient and safe point (say D-point, in front of the door) in a fully autonomous manner. In this demo, we specifically focus on the key technical component Structural Semantic Segmentation (SSS), a novel semantic segmentation-based approach to identify D-point. Unlike the classical SS, it capitalizes the common single-family house (SFH) structures to refine the misclassified region from SS results. Our results show that the detection precision of boundary between the house and pavement (indicating the door location) reaches 84%, compared to 16% achieved by SS method, and only adds 430 ms to the processing time. We leverage SSS to progressively update the D-point guiding it closer to the door (Figure 1a). Video can be found at https://youtu.be/G6I9XzCyHFQ