LAUSR

This paper describes a novel method for large-scale 3D mapping for construction cranes with an arbitrary motion of the sensor system (2D lidar and IMU) attached to the crane boom. A heavy lidar with a slowly rotating base is needed to make a large-scale map both vertically and horizontally for cranes. This sensor configuration and mapping conditions entail handling each 2D scan separately, making it difficult to adopt existing rotating 2D lidar-based methods that construct a virtual 3D scan from a set of 2D scans. In the proposed method, we introduce a complementary filter with moving average filtering for lidar pose estimation, which is more robust to severe vibration than Kalman filter-based methods. As there are only a small amount of overlaps between 2D lidar scans, we propose a map correction method based on a pose graph optimization with planar environmental constraints. We evaluate the proposed method in a simulation and a small-sized real environment and compare it with one of the state-of-the-art methods. The evaluation results reveal that the proposed method can accurately estimate the sensor poses, thereby generating a high-quality, large-scale 3D point cloud map.