AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shital Shah,Debadeepta Dey,Chris Lovett,Ashish Kapoor +3 more
- pp 621-635
TLDR
In this paper, the authors present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real-world environments, including a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g., MavLink).Abstract:
Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (eg MavLink) The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols In addition, the modular design enables various components to be easily usable independently in other projects We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flightsread more
Citations
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Journal ArticleDOI
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Longlong Jing,Yingli Tian +1 more
TL;DR: An extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos as a subset of unsupervised learning methods to learn general image and video features from large-scale unlabeled data without using any human-annotated labels is provided.
Proceedings ArticleDOI
Gibson Env: Real-World Perception for Embodied Agents
TL;DR: Gibson as discussed by the authors is a real-world environment for active agents to learn visual perception tasks in real-time and is based upon virtualizing real spaces, rather than artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings.
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Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Longlong Jing,Yingli Tian +1 more
TL;DR: Self-Supervised Learning: Self-supervised learning as discussed by the authors is a subset of unsupervised image and video feature learning, which aims to learn general image features from large-scale unlabeled data without using any human-annotated labels.
Proceedings ArticleDOI
Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer
TL;DR: In this paper, the authors take advantage of image style transfer and adversarial training to predict pixel perfect depth from a single real-world color image based on training over a large corpus of synthetic environment data.
ESIM: an Open Event Camera Simulator
TL;DR: This work presents the first event camera simulator that can generate a large amount of reliable event data, and releases an open source implementation of the simulator, which is a theoretically sound, adaptive rendering scheme that only samples frames when necessary.
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