Many MLS point cloud application scenarios, such as navigation and localization algorithms, require only static environments, but the original MLS data usually inevitably includes many dynamic objects such as moving vehicles, bicycles, and pedestrians. Therefore, these dynamic objects need to be removed before using MLS point clouds. This thesis designs an efficient and memory-friendly Octomap-based dynamic object detection and removal method for MLS data. Firstly, the original MLS data is split into multiple data frames based on the timestamp of each capture point. Each data frame is inserted into a separate Octomap along with its neighboring data frames. The free points in all Octomaps are extracted by setting an occupancy probability threshold. Second, the region of interest (ROI) related to the dynamic object is delineated by the MLS sensor mounting height and the local large vehicle height limit. Only the free points located within the ROI are retained. Then the free-point rate and the multi-return rate are calculated for each free point using a fixed radius spatial search to denoise and detect vegetation points. Finally, the KNN spatial search is used to remove vegetation points and extract dynamic objects from the free points. The proposed method is tested in four case sites in Delft, the Netherlands and its producer’s and user’s weighted average dynamic object detection and extraction accuracies are 88.004% and 82.624%, respectively. The weighted average overall accuracy is 99.833%. Compared with the original Octomap, the proposed method is 35.472% more efficient on average and can be further accelerated by parallel computing, with a maximum memory consumption of only 42.437% of the original Octomap. The implementation results and accuracy assessment demonstrate that the proposed method can be effectively applied to dynamic object detection and extraction tasks in MLS data sets in a compute-friendly and memory-friendly way.