Real-time Instance-Aware Segmentation and Semantic Mapping on Edge Devices - Lu, Junjie; Tian, Bailing; Shen, Hongming; Zhang, Xuewei (IEEE transactions on instrumentation and measurement, 2023)
Perceiving the environment semantically in real-time is challenging for unmanned aerial vehicles (UAVs) with limited computational resources. In this paper, a real-time instance-aware segmentation and semantic mapping method on small edge devices is proposed. Taking RGB-D image as input, the presented instance segmentation pipeline is able to run at the speed of 38 FPS on AGX Xavier. To achieve this, we take a lightweight object detection model as backbone and reformulate the mask generation problem as threshold regression in depth by a novel designed truncation network. After that, a probability grid map is constructed to integrate the categories of voxels and object-level entities. Objects parameterized by pose, extent, category, and point cloud are tracked and fused across frames by data association. Finally, autonomous exploration experiments of UAV are conducted to demonstrate the effectiveness of the proposed method in both simulation and real-world.