Adaptive Acquisition and Visualization of Point Cloud Using Airborne LIDAR and Game Engine

Abstract

The development of digital twin for smart city applications requires real-time monitoring and mapping of urban environments. This work develops a framework of real-time urban mapping using an airborne light detection and ranging (LIDAR) agent and game engine. In order to improve the accuracy and efficiency of data acquisition and utilization, the framework is focused on the following aspects: (1) an optimal navigation strategy using Deep Q-Network (DQN) reinforcement learning, (2) multi-streamed game engines employed in visualizing data of urban environment and training the deep-learning-enabled data acquisition platform, (3) dynamic mesh used to formulate and analyze the captured point-cloud, and (4) a quantitative error analysis for points generated with our experimental aerial mapping platform, and an accuracy analysis of post-processing. Experimental results show that the proposed DQN-enabled navigation strategy, rendering algorithm, and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of an urban environment.