5 accepted papers
The rapid evolution of retail robotics is set to transform in-store operations through advanced automation, spanning vision-based inventory tracking, order picking, packing, and restocking. Yet fine-grained product identification remains a bottleneck: assortments change, packaging evolves, and shelv…
Robots operating in everyday environments encounter a wide variety of previously unseen objects. Deep Learning methods simplify unknown object and scene segmentation by structuring inherent real-world complexities, improving visual scene understanding. However, they need vast amounts of labeled high
Robotic applications like automated order picking in warehouses or retail stores, or fetch and carry tasks in hospitals, care homes, or households rely on the capability of service robots to find and handle a specific type of object. These applications are challenging as the set of objects is very l…
Coverage Path Planning (CPP) describes the process of generating robot trajectories that fully cover an area or volume. Applications are, amongst many others, mobile cleaning robots, lawn mowing robots or harvesting machines in agriculture. Many approaches and facets of this problem have been discus…
The division of floor plans or navigation maps into single rooms or similarly meaningful semantic units is central to numerous tasks in robotics such as topological mapping, semantic mapping, place categorization, human-robot-interaction, or automatized professional cleaning. Although many map parti