Abstract
Abstract. Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95% of the measurements are labeled correctly.
Highlights
Most of todays mobile mapping systems apply active laser scanners in addition to camera systems to supplement dense 3D environment data
The mobile laser scans generated by such systems are usually of high accuracy and more detailed than airborne laser scans, which makes them suitable for change detection in urban environments, as it is required for the automatic detection of changes in public road space
The task of long-term change detection benefits from the removal of all measurements associated with mobile objects, so that only the static structures of interest remain
Summary
Most of todays mobile mapping systems apply active laser scanners in addition to camera systems to supplement dense 3D environment data. Both moving objects and the remaining background should be preserved The latter one is of further interest for the modeling process and the former can be utilized to characterize, recognize and remove non-moving instances of these mobile objects in subsequent steps. An algorithm for labeling range measurements utilizing a volumetric environment representation is proposed. The algorithm’s input consists of a set of range measurements acquired by a mobile LiDAR sensor These are organized as scans, where each scan includes mul-. Based on the occupancy probability of the voxel in question the following cases can be distinguished:
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