RSSS: Robust Structural Semantic Segmentation for Autonomous Drone Delivery to Door

Abstract

Autonomous drone delivery to door relies on a popular computer vision technique called semantic segmentation (SS) to recognize meaningful house segments and determine a precise drop-off point near the door. While this SS-based approach is effective under specific environments, it fails to perform well across other common environmental factors such as different seasons, hours of the day, weather and illumination levels. In this work, we propose Robust Structural Semantic Segmentation (RSSS), a novel patch to the existing SS solution without requiring re-training for new environments. The core idea is to “Let Strong Help Weak”, where the results of semantic segmentation obtained under favorable/strong conditions are utilized to enhance the weaker ones in adverse settings. This improvement is achieved by leveraging house structures and spatial layouts, which remain largely invariant across various environments. Our evaluation shows that RSSS outperforms the state-of-the-art methods and significantly enhances the robustness of SS and drone delivery across various environments. The dataset collected for this study is released at Github [1].