Computer-Assisted Surgery (CAS) aids the surgeon by enriching the surgical scene with additional information in order to improve patient outcome. One such aid may be the superimposition of important structures (such as blood vessels and tumors) over a laparoscopic image stream. In liver surgery, this may be achieved by creating a dense map of the abdominal environment surrounding the liver, registering a preoperative model (CT scan) to the liver within this map, and tracking the relative pose of the camera. Thereby, known structures may be rendered into images from the camera perspective. This intraoperative map of the scene may be constructed, and the relative pose of the laparoscope camera estimated, using Simultaneous Localisation and Mapping (SLAM). The intraoperative scene poses unique challenges, such as: homogeneous surface textures, sparse visual features, specular reflections and camera motions specific to laparoscopy. This work compares the efficacies of two state-of the-art SLAM systems in the context of laparoscopic surgery, on a newly collected phantom dataset with ground truth trajectory and surface data. The SLAM systems chosen contrast strongly in implementation: one sparse and feature-based, ORB-SLAM3,1{3 and one dense and featureless, ElasticFusion.4 We find that ORB-SLAM3 greatly outperforms ElasticFusion in trajectory estimation and is more stable on sequences from laparoscopic surgeries. However, when extended to give a dense output, ORB-SLAM3 performs surface reconstruction comparably to ElasticFusion. Our evaluation of these systems serves as a basis for expanding the use of SLAM algorithms in the context of laparoscopic liver surgery and Minimally Invasive Surgery (MIS) more generally.