GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

Abstract

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—GRADE. GRADE leverages Isaac’s 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 https://grade.is.tue.mpg.de .