Large-Scale Semantic Mapping

Semantic maps are an important tool to provide robots with high-level knowledge about the environment, enabling them to better react to and interact with their surroundings. However, as a single measurement of the environment is solely a snapshot of a specific time, it does not necessarily reflect the underlying semantics. In this work, we propose a method to create a semantic map of a construction site by fusing multiple daily data. The construction site is measured by an unmanned aerial vehicle (UAV) equipped with a LiDAR. We extract clusters above ground level from the measurements and classify them using either a random forest or a deep learning based classifier. Furthermore, we combine the classification results of several measurements to generalize the classification of the single measurements and create a general semantic map of the working site. We measured two construction fields for our evaluation. The classification models can achieve an average intersection over union (IoU) score of 69.2 during classification on the Sanbongi field, which is used for training, validation and testing and an IoU score of 49.16 on a hold-out testing field. In a final step, we show how the semantic map can be employed to suggest a parking spot for a dump truck, and in addition, show that the semantic map can be utilized to improve path planning inside the construction site.