生成可调控拓扑的地下环境，用于机器人自主系统严格验证
SubTGraph: Large-Scale Subterranean Environment Synthesis with Controllable Topological Variability for Robotic Autonomy Validation
- 基于用户指定结构约束，用改进迪杰斯特拉算法生成地下世界
- 构建150个高变异地下场景，支持多层机器人系统测试
- 适合机器人自主性验证、路径规划与定位算法研究者使用
地下环境是自主机器人的重要前沿，推动采矿自动化和火星熔岩管探测。由于真实地下环境难以访问，必须在逼真的仿真中强化自主系统。本文填补了缺乏大规模仿真基准平台的空白，提出SubTGraph框架，可快速生成具有高度拓扑变异性、多层次的地下环境，支持用户自定义拓扑、尺寸、纹理等参数，生成如矿井、天然洞穴、熔岩管等不同类型场景。SubTGraph通过用户指定的结构约束构建代价矩阵，驱动经典Dijkstra算法，结合DARPA世界生成器的拓扑-几何图块，程序化生成地下世界。三个机器人案例研究验证其有效性：结构语义分割与拓扑真值对比；多智能体路径规划测试以发现算法模式；LIO SLAM在复杂地下段进行压力测试，识别失效案例。代码库已开源（https://github.com/LTU-RAI/SubTGraph.git），包含150个高度变异的地下世界数据库。
原文摘要 · Abstract (English)
Subterranean (SubT) environments have been a frontier for autonomous robotics, driven by the push for automation of mining operations and the interest in planetary exploration (Martian Lava Tubes). Due to the challenges involved in accessing real SubT environments, rigorous hardening of autonomy stacks in realistic simulation environments is critical. This article fills a well-known gap, which relates to the unavailability of a large-scale simulation-based benchmarking infrastructure for rigorous statistical evaluation of robotic autonomy, due to which it is common for SubT research articles to present validation results in a few environments at best. This article presents SubTGraph, a novel framework for rapid synthesis of multi-level SubT environments with high variability, incorporating user specifications related to topology, dimensionality, textures, etc., to generate distinct environments such as operational mines, natural caves and lava tubes. SubTGraph builds a cost matrix from user-specified structural constraints to guide the classical Dijkstra algorithm to procedurally generate SubT worlds utilizing topometric tiles from the DARPA World Generator. Three robotics case-studies are investigated to demonstrate the utility of SubTGraph for rigorous validation of different layers in the robotic autonomy stack. Structural semantic segmentation is validated against topometric ground truths, multi-agent path planning is widely tested for identification of patterns and trends in the algorithm behavior and LIO SLAM is stress-tested in challenging subterranean sections to identify failure cases. The SubTGraph world creation codebase is open-sourced (https://github.com/LTU-RAI/SubTGraph.git) along with a database consisting of 150 highly variable underground worlds.
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