新方法让机器人在有未知物体时仍能准确定位，比传统方案更灵活。
Dynamic semantic VSLAM with known and unknown objects
- 用无监督分割+光流分析，区分静态与动态特征
- 在未知物体存在时性能超越传统VSLAM，已知物上媲美顶尖技术
- 适合真实场景中动态物体多变的机器人导航
传统视觉同步定位与地图构建（VSLAM）系统假设环境静止，难以应对高度动态场景。现有方法虽融合深度学习语义信息识别动态区域，但受限于需标注训练数据，无法识别未见物体。本文提出一种基于特征的语义VSLAM，可在已知与未知物体共存时检测动态特征。通过无监督分割网络实现无标签分割，再结合目标检测器识别已知类别，结合高梯度光流信息，进一步区分已知与未知物体的静态/动态属性。引入一致性检查模块进行精炼与最终分类。在公开数据集上的评估表明，当图像包含未知物体时，本方法性能显著优于传统VSLAM；而在仅含已知物体时，性能与领先语义VSLAM相当。
原文摘要 · Abstract (English)
Traditional Visual Simultaneous Localization and Mapping (VSLAM) systems assume a static environment, which makes them ineffective in highly dynamic settings. To overcome this, many approaches integrate semantic information from deep learning models to identify dynamic regions within images. However, these methods face a significant limitation as a supervised model cannot recognize objects not included in the training datasets. This paper introduces a novel feature-based Semantic VSLAM capable of detecting dynamic features in the presence of both known and unknown objects. By employing an unsupervised segmentation network, we achieve unlabeled segmentation, and next utilize an objector detector to identify any of the known classes among those. We then pair this with the computed high-gradient optical-flow information to next identify the static versus dynamic segmentations for both known and unknown object classes. A consistency check module is also introduced for further refinement and final classification into static versus dynamic features. Evaluations using public datasets demonstrate that our method offers superior performance than traditional VSLAM when unknown objects are present in the images while still matching the performance of the leading semantic VSLAM techniques when the images contain only the known objects
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