Sensor Pose Estimation and 3D Mapping for Crane Operations Using Sensors Attached to the Crane Boom

This paper describes a method for sensor pose estimation, as well creating large-scale 3D maps, construction cranes equipped with system consisting of camera, 2D lidar, and IMU. To tackle the challenges posed by crane boom’s complex motion, we utilize an Extended Kalman filter (EKF) to improve accuracy reliability estimation. By combining estimates from Visual-Inertial Navigation System (VINS) data additional IMU, estimate scale value monocular camera. value, obtained EKF, is then integrated into VINS algorithm refine previously estimated value. Slowly rotating lidar used build map. Since there limited overlap between scans, leverage align construct comprehensive Additionally, thoroughly evaluate effectiveness latest techniques, EKF-enhanced approach, in specific context operations. Through performance assessments conducted both simulated real environments, compare EKF-added state-of-the-art techniques. The evaluation results demonstrate that accurately poses, leading generation high-quality, point cloud maps cranes.