Detecting changes such as moved, removed, or new objects is the essence for numerous indoor applications in robotics such as tidying-up, patrolling, and fetch/carry tasks. The problem is particularly challenging in open-world scenarios where novel objects may appear at any time. The main idea of this paper is to detect objects from partial 3D reconstructions of interesting areas in the environment. In our pipeline we first identify planes, consider clusters on top as objects, and compute their point-pair-features. They are used to match potential objects and categorize them robustly into static, moved, removed, and novel objects even in the presence of partial object reconstructions and clutter. Our approach dissolves heaps of objects without specific object knowledge, but only with the knowledge acquired from change detection. The evaluation is performed on real-world data that includes challenges affecting the quality of the reconstruction as a result of noisy input data. We present the novel dataset ObChange for quantitative evaluation, and we compare our method against a baseline using learning-based object detection. The results show that, even with a targeted training set, our approach outperforms the baseline for most test cases. Lastly, we also demonstrate our method's effectiveness in real robot experiments.
