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arXiv 2309.12397cs.ROcs.CV

POLAR-Sim：增强NASA的POLAR数据集，用于数据驱动的月球感知与月球车仿真

POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation

University of Wisconsin-Madison（威斯康星大学麦迪逊分校）

Bo-Hsun Chen, Peter Negrut, Thomas Liang, Nevindu Batagoda, Harry Zhang, Dan Negrut

备注：
11 pages, 9 figures. This work has been submitted to the IEEE for possible publication
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AI总结：

该工作为NASA的POLAR月球数据集添加边界框与语义分割标签，并生成13种场景的数字孪生，以支持月球感知与仿真数据的无限生成。

摘要：

NASA的POLAR数据集包含约2600对高动态范围立体照片，这些照片是在13种不同的地形场景中拍摄的，包括岩石分布稀疏或密集的区域、陨石坑以及不同大小的岩石。这些照片的目的是促进机器人技术、基于AI的感知和自主导航的发展。鉴于月球极地附近的月球图像稀缺，NASA艾姆斯研究中心在地球上但受控的条件下生成了模拟月球这些区域月球车运行条件的图像。我们报告了一项旨在完成两项任务的工作成果。在任务1中，我们为NASA的POLAR数据集中的所有图像提供了边界框和语义分割信息。这项工作产生了23000个标签以及关于岩石、阴影和陨石坑的语义分割注释。在任务2中，我们生成了用于生成POLAR数据集中所有照片的13种场景的数字孪生。具体来说，对于每个场景，我们生成了与地面和岩石相关的单独网格、纹理信息和材料属性。这使得任何拥有相机模型的人都能合成与POLAR数据集13种场景中任一场景相关联的图像。实际上，人们可以根据需要生成任意数量的带有语义标签的合成图像——在场景中具有不同的位置和曝光值，太阳的不同位置，存在或不存在主动照明等。这项工作的好处是双重的。利用任务1的成果，人们可以训练和/或测试处理月球图像的感知算法。对于任务2，人们可以生成所需数量的数据，以训练和测试预期在月球条件下工作的AI算法。这项工作的所有成果都可在公共存储库中获取，以供自由使用和分发。

英文摘要：

NASA's POLAR dataset contains approximately 2,600 pairs of high dynamic range stereo photos captured across 13 varied terrain scenarios, including areas with sparse or dense rock distributions, craters, and rocks of different sizes. The purpose of these photos is to spur development in robotics, AI-based perception, and autonomous navigation. Acknowledging a scarcity of lunar images from around the lunar poles, NASA Ames produced on Earth but in controlled conditions images that resemble rover operating conditions from these regions of the Moon. We report on the outcomes of an effort aimed at accomplishing two tasks. In Task 1, we provided bounding boxes and semantic segmentation information for all the images in NASA's POLAR dataset. This effort resulted in 23,000 labels and semantic segmentation annotations pertaining to rocks, shadows, and craters. In Task 2, we generated the digital twins of the 13 scenarios that have been used to produce all the photos in the POLAR dataset. Specifically, for each of these scenarios, we produced individual meshes, texture information, and material properties associated with the ground and the rocks in each scenario. This allows anyone with a camera model to synthesize images associated with any of the 13 scenarios of the POLAR dataset. Effectively, one can generate as many semantically labeled synthetic images as desired -- with different locations and exposure values in the scene, for different positions of the sun, with or without the presence of active illumination, etc. The benefit of this work is twofold. Using outcomes of Task 1, one can train and/or test perception algorithms that deal with Moon images. For Task 2, one can produce as much data as desired to train and test AI algorithms that are anticipated to work in lunar conditions. All the outcomes of this work are available in a public repository for unfettered use and distribution.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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