AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Shah, Shital; Dey, Debadeepta; Lovett, Chris; Kapoor, Ashish

doi:10.48550/arxiv.1705.05065

Cited by 46 publications

(66 citation statements)

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“…EXPERIMENTS Dataset. For our dataset, we use the photo-realistic, multirobot AirSim [14] simulator to collect synchronized RGB, depth, semantic segmentation, and pose information from a realistic city environment with dynamic objects. Within this environment, we control a non-rigid swarm of 6 drones to various road intersections.…”

Section: Methods a Multi-agent Infilling Networkmentioning

confidence: 99%

Enhancing Multi-Robot Perception via Learned Data Association

Glaser

Liu

Tian

et al. 2021

Preprint

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In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multiview infilling for distributed semantic segmentation. This setting entails several real-world challenges, especially those relating to unregistered multi-agent image data. Solutions must effectively leverage multiple, non-static, and intermittently-overlapping RGB perspectives. To this end, we propose the Multi-Agent Infilling Network: an extensible neural architecture that can be deployed (in a distributed manner) to each agent in a robotic swarm. Specifically, each robot is in charge of locally encoding and decoding visual information, and an extensible neural mechanism allows for an uncertainty-aware and context-based exchange of intermediate features. We demonstrate improved performance on a realistic multi-robot AirSim dataset.

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Section: Methods a Multi-agent Infilling Networkmentioning

confidence: 99%

Enhancing Multi-Robot Perception via Learned Data Association

Glaser

Liu

Tian

et al. 2021

Preprint

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“…The publication [95] deals with a hybrid camera array-based autonomous landing procedure with a high field of view camera (fish-eye camera) and a tailored control strategy. The authors describe the development of their system with an initial simulation and parameter identification in Microsoft AirSim [146]. An ArUco marker was attached to the landing platform, and multiple tests validated the performance of the system in simulations and a real scenario.…”

Section: Landing the Uav On An Agvmentioning

confidence: 99%

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

Sinnemann

Boshoff

Dyrska

et al. 2022

Prod. Eng. Res. Devel.

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The cooperation of Unmanned Aerial Vehicles (UAVs) and Mobile Robot Manipulators (MRMs) offers enormous possibilities to modern industry. It paves the way for logistics, cooperative assembling or manipulation and will provide even more flexibility and autonomy to today’s manufacturing processes. Currently, some systematic literature reviews exist that provide an overview on research fields and gaps in the field of UAVs and MRMs. However, an investigation of the research landscape for combined use of UAVs and MRMs does not exist to the best of the authors’ knowledge. Therefore, in this paper, a systematic review of the current research landscape for the combined use of UAV and MRM is conducted to finally identify fields of action that need to be addressed in the future to harness the full potential.

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“…To produce this dataset, we spawned a swarm of 6 drones in the photo-realistic, multi-robot AirSim [15] simulator. We commanded the drones to move roughly together throughout the environment, capturing synchronized images along the way.…”

Section: × Rmentioning

confidence: 99%

“…To demonstrate the efficacy of our approach, we perform extensive experimentation in a photo-realistic simulation environment (AirSim [15]), specifically investigating how a group of mobile robots can communicate their observations to overcome unexpected foreground obstructions, such as occluding vegetation and wildlife. Our method increases performance on a multi-agent semantic segmentation task by an absolute 11% IoU over strong baselines, and it approaches arXiv:2107.00771v1 [cs.RO] 1 Jul 2021 upper bounds that utilize ground truth transformations across the agents' sensors, while saving significant bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking

Glaser

Liu

Tian

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we address bandwidth-limited and obstruction-prone collaborative perception, specifically in the context of multi-agent semantic segmentation. This setting presents several key challenges, including processing and exchanging unregistered robotic swarm imagery. To be successful, solutions must effectively leverage multiple non-static and intermittently-overlapping RGB perspectives, while heeding bandwidth constraints and overcoming unwanted foreground obstructions. As such, we propose an end-to-end learn-able Multi-Agent Spatial Handshaking network (MASH) to process, compress, and propagate visual information across a robotic swarm. Our distributed communication module operates directly (and exclusively) on raw image data, without additional input requirements such as pose, depth, or warping data. We demonstrate superior performance of our model compared against several baselines in a photo-realistic multi-robot AirSim environment, especially in the presence of image occlusions. Our method achieves an absolute 11% IoU improvement over strong baselines.

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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Cited by 46 publications

References 0 publications

Enhancing Multi-Robot Perception via Learned Data Association

Enhancing Multi-Robot Perception via Learned Data Association

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking

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