{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T10:03:47Z","timestamp":1777716227550,"version":"3.51.4"},"reference-count":93,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Cyber Valley Research Fund Project \u00e2\u20ac\u0153WildCap\u00e2\u20ac","award":["CyVy-RF-2020-13"],"award-info":[{"award-number":["CyVy-RF-2020-13"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>\n                    Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related information. This, combined with the difficulties of realistically simulating dynamic worlds and the insufficient photorealism, flexibility, and control options of common robotics simulation frameworks, hinders progress in (visual-)perception research for autonomous robotics. For instance, most Visual Simultaneous Localization and Mapping methods are passive, developed under a (semi-)static environment assumption, and evaluated on just a limited number of pre-recorded datasets. To address these challenges, we present a highly customizable framework built upon NVIDIA Isaac Sim for Generating Realistic and Dynamic Environments\u2014GRADE. GRADE leverages Isaac\u2019s rendering capabilities, physics engine, and low-level APIs to populate and manage realistic simulations, generate synthetic data, and evaluate online and offline robotics approaches, including Active SLAM and heterogeneous multi-robot scenarios. Within GRADE, we introduce a novel experiment repetition approach that allows environmental and scenario variations of previous simulations within physics-enabled environments, enabling flexible and continuous testing, development, and data generation. We then use GRADE to collect a high-fidelity and richly annotated synthetic video dataset of indoor dynamic environments. With that, we train detection and segmentation models for humans and successfully address the syn-to-real gap. We then benchmark state-of-the-art dynamic V-SLAM algorithms, revealing their limitations in tracking times and generalization capabilities, and evidencing that top-performing deep learning models do not necessarily lead to the best SLAM performance. Code and data are provided as open-source at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/grade.is.tue.mpg.de\">https:\/\/grade.is.tue.mpg.de<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/02783649251346211","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T02:32:28Z","timestamp":1750818748000},"page":"204-232","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0452-8761","authenticated-orcid":false,"given":"Elia","family":"Bonetto","sequence":"first","affiliation":[{"name":"Perceiving Systems Department, Max Planck Institute for Intelligent Systems"},{"name":"Flight Robotics and Perception Group, University of Stuttgart"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2521-4493","authenticated-orcid":false,"given":"Chenghao","family":"Xu","sequence":"additional","affiliation":[{"name":"Perceiving Systems Department, Max Planck Institute for Intelligent Systems"},{"name":"Civil Engineering Institute, Swiss Federal Institute of Technology Lausanne (EPFL)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0727-3031","authenticated-orcid":false,"given":"Aamir","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Perceiving Systems Department, Max Planck Institute for Intelligent Systems"},{"name":"Flight Robotics and Perception Group, University of Stuttgart"}]}],"member":"179","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_3_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117734"},{"key":"e_1_3_6_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196621"},{"key":"e_1_3_6_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073207"},{"key":"e_1_3_6_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-018-0966-3"},{"key":"e_1_3_6_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58565-5_21"},{"key":"e_1_3_6_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2860039"},{"key":"e_1_3_6_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00843"},{"key":"e_1_3_6_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECMR59166.2023.10256293"},{"key":"e_1_3_6_10_1","unstructured":"Bonetto E Ahmad A (2024) Zebrapose: zebra detection and pose estimation using only synthetic data."},{"key":"e_1_3_6_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECMR50962.2021.9568791"},{"key":"e_1_3_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2022.104102"},{"key":"e_1_3_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636814"},{"key":"e_1_3_6_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364915620033"},{"key":"e_1_3_6_15_1","volume-title":"ShapeNet: An Information-Rich 3D Model Repository","author":"Chang AX","year":"2015","unstructured":"Chang AX, Funkhouser T, Guibas L, et al. 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