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Dataset . 2022
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
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Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset

Authors: Zhanteng Xie; Philip Dames;

Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset

Abstract

Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames 1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points 2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin 3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin The relevant code is available at: OGM prediction: https://github.com/TempleRAIL/SOGMP OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch

{"references": ["Karnan, Haresh, et al. \"Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation.\" arXiv preprint arXiv:2203.15041 (2022)."]}

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Keywords

Robot Navigation, Environment Prediction, Occupancy Grid Map

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