Abstract
Abstract. The classification of Mobile Laser Scanner (MLS) data is challenging due to the combination of high variation in point density with a high variation of object appearances. The way how objects appear in the MLS data highly depends on the speed and orientation of the mobile mapping platform and the occlusion by other vehicles. There have been many approaches dealing with the geometric and contextual appearance of MLS points, voxels and segments to classify the MLS data. We present a completely different strategy by fusing the MLS data with a large scale topographic map. Underlying assumption is that the map delivers a clear hint on what to expect in the MLS data, at its approximate location. The approach presented here first fuses polygon objects, such as road, water, terrain and buildings, with ground and non-ground MLS points. Non-ground MLS points above roads and terrain are further classified by segmenting and matching the laser points to corresponding map point objects. The segmentation parameters depend on the class of the map points. We show that the fusion process is capable of classifying MLS data and detecting changes between the map and MLS data. The segmentation algorithm is not perfect, at some occasions not all the MLS points are correctly assigned to the corresponding map object. However, it is without doubt that the proposed map fusion delivers a very rich labelled point cloud automatically, which in future work can be used as training data in deep learning approaches.
Highlights
Mobile laser scanner (MLS) data consists of a huge collection of points captured from a streetwise perspective
Additional benefit of such a map based labelling is the automatic generation of detailed training samples which can be used as input in a deep learning classification network
Point density can be considered rather coarse for MLS data with 300 points per square meter below the sensor to 10 points per square meter on the ground surface at 30 meter distance from the sensor
Summary
Mobile laser scanner (MLS) data consists of a huge collection of points captured from a streetwise perspective. Point based methods calculate features and relations from point neighborhoods (Weinmann et al, 2015), and use descriptors in supervised classification algorithms, like in Luo et al 2018. In this research MLS data is fused with a 2D large scale topographic map to support the labelling process. Main research challenge described here is the correct fusion of 2D map data with 3D point clouds, i.e. to correctly assign laser points to map objects. Procedure results in discrepancies between map and MLS data, which can directly be used for map updating Additional benefit of such a map based labelling is the automatic generation of detailed training samples which can be used as input in a deep learning classification network. In this paper the focus is on the design of a method that smartly fuses laser points with their corresponding map objects, by looking at semantic characteristics of the objects
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