The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different scanner confgurations and scanner hardware settings will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Insufficient knowledge of the factors in uencing MMS point density means that defning point density in project specifications is a complicated process. The objectives of this thesis are to calculate point density, to assess MMS laser scanner configuration and hardware settings and to benchmark a selection of MMSs in terms of their point density. The calculation methods involve a combination of algorithms applying 3D surface normals and 2D geometric formulae and outputs profile angle, profile spacing, point spacing and point density. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. These algorithms are combined in a system called the Mobile Mapping Point Density Calculator (MIMIC). MIMIC is then applied in a series of tests identifying the recommended MMS laser scanner configuration and scanner hardware settings for near side infrastructure. The in uence that the scanner orientation and location on the MMS has on point density is quantified, resulting in a recommended MMS laser scanner configuration. A series of benchmarking tests assess the performance of one commercial and two theoretical MMSs in terms of their point density. The recommended configuration identified in the previous tests allows a low specification MMS to increase its performance in relation to a higher specification MMS. The benchmarking tests also highlight that a high pulse repetition rate is preferable to a high mirror frequency for maximising point density. The findings in this thesis enable a MMS to be configured to maximise point density for specific targets. Researchers can utilise MIMIC to tailor their automated algorithm's point density requirements for specific targets.