四足机器人实时构建带语义的全局物体地图
Online Object-Level Semantic Mapping for Quadrupeds in Real-World Environments
- 融合视觉与距离数据，动态合并同类物体
- 跨帧关联重复检测，保持物体实例一致
- 输出可被规划器读取的紧凑语义图层
我们提出一种面向四足机器人在真实室内环境中的在线语义物体映射系统，将传感器检测结果转化为全局地图中带有名称的物体。运行过程中，映射器结合范围几何与相机检测结果，合并同一帧内的共位检测，并将重复出现的检测关联为跨帧持续存在的物体实例。即使物体暂时不可见，其仍保留在地图中，后续再次观测会更新同一实例而非生成新条目。输出为可查询类别、位姿和置信度的紧凑物体层，与占用栅格地图集成，可供规划器使用。在机器人实测中，该图层在视角变化下保持稳定。
原文摘要 · Abstract (English)
We present an online semantic object mapping system for a quadruped robot operating in real indoor environments, turning sensor detections into named objects in a global map. During a run, the mapper integrates range geometry with camera detections, merges co-located detections within a frame, and associates repeated detections into persistent object instances across frames. Objects remain in the map when they are out of view, and repeated sightings update the same instance rather than creating duplicates. The output is a compact object layer that can be queried (class, pose, and confidence), is integrated with the occupancy map and readable by a planner. In on-robot tests, the layer remained stable across viewpoint changes.
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