3D Object Detection for Aerial Platforms via Edge Computing: An Experimental Evaluation

LiDAR is rapidly emerging as a central sensor in many applications involving autonomous navigation. However, the execution of state-of-the-art neural models for the analysis of point clouds produced by LiDARs necessitates considerable computing power, energy and memory. As a consequence, real-time analysis – e.g., 3D object detection – on resource-constrained mobile platforms such as Unmanned Aerial Vehicles (UAV) is often impractical. In this paper, we evaluate the feasibility of real-time LiDAR-based object detection for UAVs using measures and data obtained from a real-world deployment. First, we demonstrate that state-of-the-art neural models for 3D object detection cannot be executed even in relatively powerful embedded computers suitable for airborne drones, such as the NVIDIA Jetson Nano. Then, we focus our attention on edge computing, where the UAV offloads the execution of the analysis model to a compute-capable device (an edge server) positioned at the network edge. The key challenge is that point clouds generated by LiDARs have a large size (1.2MB per point cloud frame). We evaluate the overall capture-to-output delay of a remote analysis loop experimentally for WiFi and using expected data rate for cellular LTE environments. Finally, we evaluate the performance of 3D object detection on available datasets for autonomous vehicles and emphasize the challenges posed by the ability of UAVs to move in the 3D space. With our LiDAR-UAV system, we achieved detections with averages of 86% accuracy, 71.6% precision, and 55.14% recall outputted with an average end-to-end delay of 1920ms.