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• W2981104003 abstract "This paper presents a Teach & Repeat (T&R) algorithm from stereo and inertial data, targeting Unmanned Aerial Vehicles with limited on-board computational resources. We propose a tightly-coupled, relative formulation of the visual-inertial constraints that fits the T&R application. In order to achieve real-time operation on limited hardware, we constraint it to motion-only visual-inertial Bundle Adjustment and solve for the minimal set of states. For the repeat phase, we show how to generate a trajectory and smoothly follow it with a constantly changing reference frame. The proposed method is validated with the sequences of the EuRoC dataset as well as within a simulated environment, running on a standard laptop PC and on a low-cost Odroid X-U4 computer." @default.
• W2981104003 created "2019-10-25" @default.
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• W2981104003 date "2019-09-01" @default.
• W2981104003 modified "2023-10-17" @default.
• W2981104003 title "Visual-Inertial Teach & Repeat for Aerial Robot Navigation" @default.
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• W2981104003 doi "https://doi.org/10.1109/ecmr.2019.8870926" @default.
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