Laparoscopic Liver Resection (LLR) induces less trauma to the patient compared to open surgery. Thus the patient recovers faster which in return reduces the costs to the healthcare system. However the use of LLR remains limited. This is because of three challenges. First, controlling intraoperative bleeding using laparoscopic instruments requires advanced technical skills. Second, the surgeon cannot manually palpate the liver and thus cannot locate the tumours and their resection margins easily. Consequently this raises a risk of inadequate resection on the patient’s liver such as the removal of too much healthy tissue and the leaving of some cancerogenous tissue behind. Third, laparoscopic ultrasonography (LUS), the only tool for intraoperative subsurface imaging which allows real-time tumour localisation, has a long learning curve. In order to ease LLR, augmented reality (AR) based methods relying on preoperative data were proposed. These AR-based methods predict the location of the tumours by overlaying the preoperative data onto the laparoscopy image. Although they are useful, they are neither real-time nor automatic. Thus this project strives at enabling automatic and real-time AR guidance during LLR. To do so, it will combine the intraoperative measurements of the LUS probe, whose movement is controlled by a robot, with the preoperative data, and then overlay the combined data onto the laparoscopy images.