The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption.

Vehicle-related ground occlusion is a common problem in MLS data. This study aims to design a detection and reconstruction method of static vehicle-related ground occlusion for MLS data. Ground extraction and vehicle segmentation are performed on the input point cloud data in advance. Then an α-shape boundary based on the prior vehicle geometry is designed to split non-ground empty area and ground occlusions. The occlusion is detected and matched with its corresponding vehicle using the relative position between them. This relative position relation and the height difference are used to detect the curb direction as the local road direction. Finally, the occlusions are reconstructed using two different methods: (1) a cell-based linear interpolation and (2) a point-based mathematical morphology. The methodology is tested by original scanned data and multi-temporal evaluation data captured from a residential area in Delft, the Netherlands with vehicle-mounted LiDAR sensors. The result shows that all occlusions cause by vehicles are successfully detected and the curb (road) direction is correctly extracted in most of the occluded areas. Both reconstructed results can visually integrate the original scanned data and recover the curb structure. The reconstruction errors of the linear interpolation method are 0.045 m in the z-axis direction and 0.051 m in total and the reconstruction errors of mathematical morphology are 0.048 m in the z-axis direction and 0.052 m in total.