Automation of inspection tasks is crucial for the development of the power industry, where MAVs have shown a great potential. Self-localization in this context remains a key issue, and is the main subject of this work. This article presents a methodology to obtain complete 3D local pose estimates in electric tower inspection tasks with MAVs, using an on-board sensor setup consisting of a 2D LiDAR, a barometer sensor and an IMU. First, we present a method to track the tower's cross-sections in the laser scans, and give insights on how this can be used to model electric towers. Then, we show how the popular ICP algorithm, that is typically limited to indoor navigation, can be adapted to this scenario, and propose two different implementations to retrieve pose information. This is complemented with attitude estimates from the IMU measurements, based on a gain-scheduled non-linear observer formulation. An altitude observer to compensate for barometer drift is also presented. Finally, we address velocity estimation with views to feedback position control. Validations based on simulations and experimental data are presented.