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A novel approach for robust Unmanned Aerial Vehicle (UAV) altitude estimation relying on laser measurements that is designed for use in complex indoor environments is proposed in this paper. Specifically, we aim to design a system with general usability inside multi-floor buildings. The multi-floor buildings are characterized by areas lacking distinct vertical geometric features to be used as reference by 3D Light Detection and Ranging (LiDAR) localization algorithms, and by areas with either flat floors or limited areas with inconsistent ground elevation. The proposed approach solves the problem of adaptive fusion of data from multiple sources with apriori-unknown confidence dependent on the current environmental properties. Whenever the environment contains enough geometric structure, altitude data from a 3D LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm are utilized. In environments that are too symmetrical for reliable SLAM operation, the approach relies mostly on measurements from a downward-facing 1D laser rangefinder, while simultaneously detecting inconsistent ground elevation areas. These measurements are fused with barometer data, Inertial Measurement Unit (IMU) data, and information from the UAV position controllers. Furthermore, our approach correctly handles the measurement delay caused by 3D LiDAR data processing that significantly differs from other sensor delays. The performance of the proposed approach has been validated in complex simulations and real-world experiments with the produced altitude estimate utilized in the control loop of the UAV. The proposed approach is released as open-source as part of the MRS UAV System. • A robust UAV altitude estimation method for complex indoor environments. • Adaptive fusion of 3D LiDAR SLAM, 1D rangefinder, IMU, and controller data. • Modular re-prediction algorithm for handling delayed data. [ABSTRACT FROM AUTHOR]