Reconstructed 3D ultrasound volume provides more context information than a sequence of 2D scanning frames, which is desirable for various clinical applications such as ultrasound-guided prostate biopsy. Nevertheless, 3D volume reconstruction from freehand 2D scans is very challenging, especially without external tracking devices. Recent deep learning-based methods demonstrate the potential of directly estimating inter-frame motion between consecutive ultrasound frames. However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings. In this paper, we formulate the difference in data acquisition as domain shift and propose a novel domain adaptation strategy to adapt deep learning algorithms to data acquired using a different transducer. Specifically, feature extractors generating transducer-invariant features get trained by minimizing the discrepancy between image features of paired samples in a latent space. Our results show that the proposed domain adaptation method can successfully align different feature distributions while preserving the transducer-specific information for universal freehand ultrasound volume reconstruction.