This report proposes a combined optimal control and perception framework for Micro Aerial Vehicle (MAV) autonomous navigation in novel indoor enclosed environments, relying exclusively on on-board sensor data. We use privileged information from a simulator to generate optimal waypoints in 3D space that our perception system learns to imitate. The trained learning based perception module in turn is able to generate similar obstacle avoiding waypoints from sensor data (RGB + IMU) alone. We demonstrate the efficacy of the framework across novel scenes in the iGibson simulation environment.

翻译：本报告提议在新型室内封闭环境中,完全依靠机载传感器数据,为微型航空飞行器(MAV)自主导航提供一个综合最佳控制和感知框架,我们利用模拟器提供的特惠信息,在3D空间产生最佳路径点,我们的感知系统学会模仿。经过培训的基于学习的感知模块反过来又能够产生类似的障碍,避免传感器数据(RGB+IMU)的路径点。我们在iGibson模拟环境中展示了这个框架在新场景中的功效。