  To autonomously navigate and plan interactions in real-world environments,
robots require the ability to robustly perceive and map complex, unstructured
surrounding scenes. Besides building an internal representation of the observed
scene geometry, the key insight toward a truly functional understanding of the
environment is the usage of higher-level entities during mapping, such as
individual object instances. We propose an approach to incrementally build
volumetric object-centric maps during online scanning with a localized RGB-D
camera. First, a per-frame segmentation scheme combines an unsupervised
geometric approach with instance-aware semantic object predictions. This allows
us to detect and segment elements both from the set of known classes and from
other, previously unseen categories. Next, a data association step tracks the
predicted instances across the different frames. Finally, a map integration
strategy fuses information about their 3D shape, location, and, if available,
semantic class into a global volume. Evaluation on a publicly available dataset
shows that the proposed approach for building instance-level semantic maps is
competitive with state-of-the-art methods, while additionally able to discover
objects of unseen categories. The system is further evaluated within a
real-world robotic mapping setup, for which qualitative results highlight the
online nature of the method.
