Data set for the simulator experiment in the PLOS ONE article "Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis"
收藏DataCite Commons2020-08-30 更新2024-07-27 收录
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https://figshare.com/articles/Data_set_for_the_simulator_experiment_in_the_PLOS_ONE_article_Bio-inspired_visual_self-localization_in_real_world_scenarios_using_Slow_Feature_Analysis_/5822043/1
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资源简介：
Data set for the simulator experiment in the PLOS ONE article:<br>"Bio-inspired visual self-localization in real world scenarios<br>using Slow Feature Analysis"<br><br>Images 'panorama_0.png' - 'panorama_628.png' are panoramic images rendered on<br>an equidistant grid in a simulator environment.<br><br>Sequences for the training- and test-set were created artificially by<br>sampling successive image/coordinate pairs from the grid.<br><br>The files 'train_sequence.csv' and 'test_sequence.csv' contain the image file<br>names and corresponding coordinates for the respective sets.
本数据集用于发表于PLOS ONE期刊的论文《基于慢特征分析（Slow Feature Analysis）的真实场景仿生视觉自定位》中的模拟器实验。
图像文件panorama_0.png至panorama_628.png为在模拟器环境的等距网格上渲染得到的全景图像。
训练集与测试集的序列均通过从该网格中采样连续的图像-坐标对人工生成。
文件train_sequence.csv与test_sequence.csv分别包含对应数据集的图像文件名及配套坐标信息。
提供机构：
figshare
创建时间：
2018-01-25
