For the Thrill of it All: A bridge among Linux, Robot Operating System, Android and Unmanned Aerial Vehicles

Civilian Unmanned Aerial Vehicles (UAVs) are becoming more accessible for domestic use. Currently, UAV manufacturer DJI dominates the market, and their drones have been used for a wide range of applications. Model lines such as the Phantom can be applied for autonomous navigation where Global Positioning System (GPS) signals are not reliable, with the aid of Simultaneous Localization and Mapping (SLAM), such as monocular Visual SLAM. In this work, we propose a bridge among different systems, such as Linux, Robot Operating System (ROS), Android, and UAVs as an open-source framework, where the gimbal camera recording can be streamed to a remote server, supporting the implementation of an autopilot. Finally, we present some experimental results showing the performance of the video streaming validating the framework.Comment: Presented at WEC-UFPR 201

Kite: Automatic speech recognition for unmanned aerial vehicles

This paper addresses the problem of building a speech recognition system attuned to the control of unmanned aerial vehicles (UAVs). Even though UAVs are becoming widespread, the task of creating voice interfaces for them is largely unaddressed. To this end, we introduce a multi-modal evaluation dataset for UAV control, consisting of spoken commands and associated images, which represent the visual context of what the UAV "sees" when the pilot utters the command. We provide baseline results and address two research directions: (i) how robust the language models are, given an incomplete list of commands at train time; (ii) how to incorporate visual information in the language model. We find that recurrent neural networks (RNNs) are a solution to both tasks: they can be successfully adapted using a small number of commands and they can be extended to use visual cues. Our results show that the image-based RNN outperforms its text-only counterpart even if the command-image training associations are automatically generated and inherently imperfect. The dataset and our code are available at http://kite.speed.pub.ro.Comment: 5 pages, accepted at Interspeech 201