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

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.

Vegetation structure can profoundly influence patterns of abundance, distribution, and reproduction of herbivorous insects and their susceptibility to natural enemies. The three main structural traits of herbaceous vegetation are density, height, and connectivity. This study determined the herbivore response to each of these three parameters by analysing oviposition patterns in the field and studying the underlying mechanisms in laboratory bioassays. The generalist leaf beetle, Galeruca tanaceti L. (Coleoptera: Chrysomelidae), preferentially deposits its egg clutches on non-host plants such as grasses. Earlier studies revealed that oviposition within structurally complex vegetation reduces the risk of egg parasitism. Consequently, leaf beetle females should prefer patches with dense, tall, or connected vegetation for oviposition in order to increase their reproductive success. In the present study, we tested the following three hypotheses on the effect of stem density, height, and connectivity on oviposition: (1) Within habitats, the number of egg clutches in areas with high stem densities is disproportionately higher than in low-density areas. The number of egg clutches on (2) tall stems or (3) in vegetation with high connectivity is higher than expected for a random distribution. In the field, stem density and height were positively correlated with egg clutch presence. Moreover, a disproportionately high presence of egg clutches was determined in patches with high stem densities. Stem height had a positive influence on oviposition, also in a laboratory two-choice bioassay, whereas stem density and connectivity did not affect oviposition preferences in the laboratory. Therefore, stem height and, potentially, density, but not connectivity, seem to trigger oviposition site selection of the herbivore. This study made evident that certain, but not all traits of the vegetation structure can impose a strong influence on oviposition patterns of herbivorous insects. The results were finally compared with data on the movement patterns of the specialised egg parasitoid of the herbivore in comparable types of vegetation structure.

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