Semantic Mapping of Construction Site From Multiple Daily Airborne LiDAR Data

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.