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- W4285167557 abstract "Significant research issues and experimental possibilities on autonomous vehicles are in vogue around the world. Among these, collision-free navigation has constituted one of the significant fields to research. Along with the vigorous emergence of definite sensors, challenges are coming in new avenues. This paper proposes a keen way to detect on-route obstacles using training of model through adversarial neural network along with 3D reconstruction of a surrounding under GPS-denied Indoor Environment (IE) using point cloud map. The depth sensor used here has been systematically analysed to tackle the challenge. This paper also studies the possible challenges and hurdles faced by the customized mobile robot to create point cloud map using depth sensor integrated with Robot Operating System (ROS) platform to reconstruct the surrounding. The reconstructed visuals been used as memory trail of environment along with visual reference. This array of references would prove to be credential for path planning algorithm and promises finding near optimal collision-free Indoor Mobile Robot Navigation (IMRN)." @default.
- W4285167557 created "2022-07-14" @default.
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- W4285167557 date "2022-01-01" @default.
- W4285167557 modified "2023-09-30" @default.
- W4285167557 title "Adversarial Surround Localization and Robust Obstacle Detection with Point Cloud Mapping" @default.
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- W4285167557 doi "https://doi.org/10.1007/978-981-19-3089-8_10" @default.
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