Open AccessPosted Content
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
TL;DR: A new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real world and that is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.
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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 (e.g. 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 flights.
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Citations
Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving
TL;DR: Sim-on-Wheels as mentioned in this paper is a vehicle-in-loop framework to test autonomous vehicles' performance in the real world under safety-critical scenarios, but the events it sees are virtual.
Introspective Perception for Mobile Robots
Sadegh Rabiee,Joydeep Biswas +1 more
TL;DR: In this paper , the authors propose an approach for predicting accurate estimates of the uncertainty of perception algorithms deployed on mobile robots by exploiting sensing redundancy and consistency constraints naturally present in the data collected by a mobile robot.
Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach
Vignesh Kottayam Viswanathan,Björn Lindqvist,Sumeet Satpute,Christoforos Kanellakis,George Nikolakopoulos +4 more
TL;DR: This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions by investigating the capabilities of Unmanned Aerial Vehicles equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments.
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•Posted Content
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data
TL;DR: This paper implements a simple distance calculation approach based on camera intrinsics and 2D bounding box size, a self-supervised, and a supervised learning approach for depth estimation, and evaluates the detection pipeline on simulator data and a real world sequence from an autonomous vehicle on a race track.
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Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
Prajit KrisshnaKumar,Jhoel Witter,S. Paul,Han-Seon Cho,Karthik Dantu,Souma Chowdhury +5 more
TL;DR: UAM-VSM leverages graph learning to generate decision-support policies for vertiport schedule management, improving safety, reducing delays and optimizing battery consumption.
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TL;DR: Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots.
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John C. Butcher
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