Abstract-Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available.
India has more than 1500 1 languages, with 30 of them spoken by more than one million native speakers. Most of them are low-resource and could greatly benefit from speech and language technologies. Building speech recognition support for these low-resource languages requires innovation in handling constraints on data size, while also exploiting the unique properties and similarities among Indian languages. With this goal, we organized a low-resource Automatic Speech Recognition challenge for Indian languages as part of Interspeech 2018. We released 50 hours of speech data with transcriptions for Tamil, Telugu and Gujarati, amounting to a total of 150 hours. Participants were required to only use the data we released for the challenge to preserve the low-resource setting, however, they were not restricted to work on any particular aspect of the speech recognizer. We received 109 submissions from 18 research groups and evaluated the systems in terms of Word Error Rate on a blind test set. In this paper we summarize the data, approaches and results of the challenge.
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