For robotic interaction in an environment shared with multiple agents, accessing a volumetric and semantic map of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map representation needs to account for.To this end, we propose panoptic multi-TSDFs, a novel representation for multi-resolution volumetric mapping over long periods of time. By leveraging high-level information for 3D reconstruction, our proposed system allocates high resolution only where needed. In addition, through reasoning on the object level, semantic consistency over time is achieved. This enables to maintain up-to-date reconstructions with high accuracy while improving coverage by incorporating and fusing previous data. We show in thorough experimental validations that our map representation can be efficiently constructed, maintained, and queried during online operation, and that the presented approach can operate robustly on real depth sensors using non-optimized panoptic segmentation as input.
翻译:对于在与多个代理商共享的环境中进行机器人互动而言,访问一个体积图和语义图对场景至关重要,然而,这种环境不可避免地会受到长期变化的影响,而地图的显示需要对此作出解释。 为此,我们提议采用全光多面多面TSDF,这是长时期多分辨率量图绘制的新型代表。通过利用高层次信息进行三维重建,我们提议的系统只在必要时分配高分辨率。此外,通过对目标水平的推理,在一段时间内实现语义一致性。这样可以保持最新的精确度重建,同时通过纳入和使用先前的数据来改进覆盖范围。我们在彻底的实验性验证中显示,我们的地图显示,在网上操作期间,我们的分布可以有效地构建、维持和查询,而且所提出的方法可以在实际深度传感器上以非优化的光谱分割作为输入进行强有力的操作。