Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely. However, the lack of coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces places the burden of traversability estimation heavily on the mobile robot. This paper explores the use of a self-supervised one-shot texture segmentation framework and an RGB-D camera to achieve robust drivable area segmentation. With a fast inference speed and compact size, the developed model, MOSTS is ideal for real-time robot navigation and various embedded applications. A benchmark study was conducted to compare MOSTS's performance with existing one-shot texture segmentation models to evaluate its performance. Additionally, a validation dataset was built to assess MOSTS's ability to perform texture segmentation in the wild, where it effectively identified small low-lying objects that were previously undetectable by depth measurements. Further, the study also compared MOSTS's performance with two State-Of-The-Art (SOTA) indoor semantic segmentation models, both quantitatively and qualitatively. The results showed that MOSTS offers comparable accuracy with up to eight times faster inference speed in indoor drivable area segmentation.

翻译：确定可行驶区域（即自由空间分割）对于移动机器人在室内环境中安全导航至关重要。然而，室内空间缺乏连贯的标志和结构（如车道、路缘等），使得可通行性估计的负担主要落在移动机器人身上。本文探索了利用自监督单次纹理分割框架与RGB-D相机实现稳健的可行驶区域分割。所开发的MOSTS模型凭借快速推理速度和紧凑尺寸，成为实时机器人导航及多种嵌入式应用的理想选择。通过基准研究，将MOSTS的性能与现有单次纹理分割模型进行对比评估。此外，构建验证数据集以评估MOSTS在野外场景中进行纹理分割的能力——该模型能有效识别此前深度测量无法探测的小型低矮物体。进一步，本研究还从定量和定性两个维度将MOSTS与两种最先进(SOTA)室内语义分割模型进行比较。结果表明，在室内可行驶区域分割任务中，MOSTS在提供可比精度的同时，推理速度提升达八倍。