Precise landings on other bodies require more than just dead reckoning using an inertial measurement unit on-board the lander. If navigation of the lander with respect to a planetary surface is desired, so-called crater detection and crater-matching algorithms might be a valuable asset to find the inertial position of the vehicle using terrain relative navigation techniques. This would enable landing close to an inertially defined landing site, which could, for example, be a surface asset of a previous mission. With the desire to reduce the landing ellipse size, more precise knowledge of the inertial state of the lander is required. Based on an extensive literature review, six different algorithms were implemented to assess the performance of these. This assessment will aid the selection of crater-detection techniques for future precision landing missions. To compare the different algorithms trade-off criteria have been established. The following criteria are assessed: 1) True detection rates 2) False detection rates 3) Accuracy: as the reference maps usually have rather high resolution, inaccuracies of just a few pixels can cause large errors. 4) Run-time: the algorithm should be on-board capable. Moreover, the robustness of the algorithms was investigated. It was found that all algorithms are capable of performing the task of extracting sufficient craters for localising the landing vehicle with respect to a surface map. A method based on extracting and clustering lit pixels delivered the most promising results for the overall detections, whereas, the machine-learning based algorithms showed slightly better robustness.