Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole. To represent this variability, we propose the Variable Scene Graph (VSG), which augments existing 3D Scene Graph (SG) representations with the variability attribute, representing the likelihood of discrete long-term change events. We present a novel method, DeltaVSG, to estimate the variability of VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term dataset, showing notable improvements in this novel task over existing approaches. Our method DeltaVSG achieves an accuracy of 77.1% and a recall of 72.3%, often mimicking human intuition about how indoor scenes change over time. We further show the utility of VSG prediction in the task of active robotic change detection, speeding up task completion by 66.0% compared to a scene-change-unaware planner. We make our code available as open-source.
翻译:许多应用程序要求机器人在与其它物剂(如人类或其他机器人)共享的环境中操作。 但是,这种共享场景通常会受到不同种类的长期语义场景变化的影响。 因此,模型和预测这些变化的能力对于机器人的自主性至关重要。 在这项工作中,我们正式确定语义场景变异性估计任务,并确定语义场景变化的三大类型:物体位置的变化、其语义状态或整个场景的构成。为了反映这种变异性,我们提议了变量Scene Graph(VSG),该图将现有的3D Scene图形(SG)与变异性属性相加,代表离异的长期变化事件的可能性。我们提出了一个新颖的方法,即DeltaVSG(DelVSG),以监督的方式估计VSG的变异性。我们评估了3RScan长期数据集的方法,显示了现有方法的显著改进。我们的方法实现了77.1%的准确度和72.3%的回顾,这常常影响人类对室内场景变异性图的属性,代表着不同长期变化的可能性。我们进一步展示了VSG(Vral-ch)的快速变换任务,我们用VSG(O)的进度变换)的进度,我们可以进行。</s>