As new modelling technologies are developed, it must be tested whether or not these technologies can be used to facilitate forest inventory data acquisition. SLAM (Simultaneous Localization and Mapping) is a class of algorithms that use LiDAR (Light Detection and Ranging) sensors to localize a scanner as it is in use. This means that SLAM allows the forest to be scanned in a mobile laser scanning environment, saving time in contrast to the existing LiDAR technique called terrestrial laser scanning. The greater objective is to determine whether SLAM LiDAR data is accurate enough to be considered a viable alternative to the traditional measurements for tree metrics. In this project, we will be focusing specifically on 3 tree metrics, these are: 1) Diameter at Breast-Height (DBH), the diameter of a tree at 1.3m high, 2) Tree Height, and 3) Tree Stems, being the amount of trunks differentiated at a height lower than 1.3m. We will also determine whether accuracy fluctuates between successional stages, the 5 most common species, and if the method remains accurate for larger trees. This is done by measuring nine plots, 3 of each successional stage using both techniques, with SLAM LiDAR-based measurements being the "test" value and the traditional measurements being considered the "true" value. Much of the measured differences between the method appear to be centered around 0, with a few outliers for each successional stage and size category. It is found that LiDAR SLAM is a reliable method for DBH and stem number extraction in tropical dry forest inventories. In addition, when applying a bias correction to LiDAR SLAM collected tree height, it can also be considered a reliable method of measurement.