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-variance data for training. Acquiring these labels for rich real-world data requires significant manual effort, especially for segmentation masks. Although interactive segmentation accelerates this process, these methods still require substantial manual interaction, and the creation of large datasets remains labor-intensive. Consequently, there is a lack of diverse, high-quality datasets for unknown object instance segmentation in everyday environments. This research proposes a semi-automatic, RGB-only algorithmic pipeline for annotating novel objects, reducing manual effort to iteratively placing objects in the scene. We investigate several change detection-based approaches, including remote sensing change detection methods (TTP model), the DeepBackgroundMattingV2 image matting model, and the Segment Anything Model (SAM1 + SAM2) prompted with automatically extracted change regions. We propose the novel ILIS dataset to evaluate these methods in challenging everyday scenes, displaying reliable automatic mask proposal performance of up to 0.9549 mIoU and 0.9565 boundary F1 score. This highlights the potential of this method to accelerate large-scale dataset creation, saving at least 27.27 hours per 1,000 images by eliminating manual annotations.

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 large and varies over time. Despite its significance, there is no suitable universal large-scale dataset available from the retail domain, which allows for a principled analysis of all relevant robotics research aspects in that field. Hence, this paper introduces a novel dataset of more than 1,000 retail objects, including color images, 3D scans, and high-resolution textured 3D models of individual objects, synthetic scenes and real settings, which covers the specifics of the retail domain. The dataset was designed to serve researchers in all relevant robotics tasks in retail like 3D reconstruction and object modeling, large-scale object classification and instance detection including incremental learning and fine-grained detection, text reading, logo detection, semantic grounding and affordance detection, grasp analysis and manipulation planning, as well as digital twinning and virtual environments. Based on synthetic RGB images of scenes created from the 3D models, two exemplary use cases are examined in this paper to demonstrate the benefits of the dataset: we evaluate the state-of-the-art incremental object detection method InstanceNet and a few-shot fine-grained object classification method. The results prove the suitability of InstanceNet for incremental object detection on large datasets and are promising for the few-shot object classification system.

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 discussed in literature but despite the availability of several surveys on the topic there is little work on quantitative assessment and comparison of different coverage path planning algorithms. This paper analyzes six popular off-line coverage path planning methods, applicable to previously recorded maps, in the setting of indoor coverage path planning on room-sized units. The implemented algorithms are thoroughly compared on a large dataset of over 550 rooms with and without furniture.

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 partitioning algorithms have been proposed for various applications there is a lack of comparative studies on these different algorithms. This paper surveys the literature on room segmentation and provides four publicly available implementations of popular methods, which target the semantic mapping domain and are tuned to yield segmentations into complete rooms. In an attempt to provide new users of such technologies guidance in the choice of map segmentation algorithm, those methods are compared qualitatively and quantitatively using several criteria. The evaluation is based on a novel compilation of 20 challenging floor plans.