Improving trajectory estimation using 3D city models and kinematic point clouds

Abstract

Accurate and robust positioning of vehicles in urban environments is of high importance for autonomous driving or mobile mapping. In mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using global navigation satellite systems are targeted. This requirement is often not guaranteed in shadowed cities where global navigation satellite system signals are usually disturbed, weak or even unavailable. We propose a novel approach which incorporates prior knowledge (i.e., a 3D city model of the environment) and improves the trajectory. The recorded point cloud is matched with the semantic city model using a point‐to‐plane iterative closest point method. A pre‐classification step enables an informed sampling of appropriate matching points. Random forest is used as classifier to discriminate between facade and remaining points. Local inconsistencies are tackled by a segmentwise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The general applicability of the method implemented is demonstrated on an inner‐city data set recorded with a mobile mapping system.

Semi-Automatic Town-Scale 3D Mapping Using Building Information From Publicly Available Maps

“…Although these studies improved 2D SLAM for generating globally consistent maps in urban areas by using publicly available maps, they did not address the issue of 3D map modification. [22] modifies the SLAM by alignment with building wall and 3D LiDAR data. This method is similar to our method, but it assumes that the 3D city model and sensor observations are accurate.…”

Unlocking Point Cloud Potential: Fusing MLS Point Clouds With Semantic 3d Building Models While Considering Uncertainty

“…According to our meticulous research, only a few research groups specifically tackled the coregistration and matching of point clouds and semantic 3D city models , Goebbels et al, 2019, Lucks et al, 2021. While incorporate a Mixed Integer Linear Program to find correspondences between modalities, the latest work uses a modified ICP point-to-plane (Goebbels et al, 2019).…”

“…This implies the usage of radiometric features for prefiltering (Goebbels et al, 2019) that may remove valid building features. (Lucks et al, 2021) incorporate the ICP point-to-plane algorithm for matching of MLS point clouds and semantic 3D city models. To increase the matching accuracy, they introduce random forest to select only point clouds' points depicting fac ¸ades.…”

“…The predecessors assume absolute 3D point accuracy of point clouds at a fixed value , Goebbels et al, 2019, Lucks et al, 2021 that is believed to limit the scalability of the developed methods. Moreover, the analysis of the 3D models' coverage by point clouds is not presented , Lucks et al, 2021.…”

“…However, MLS point clouds' low absolute 3D point accuracy hinders their usability in tasks such as a refinement of 3D city models wherein overlying datasets' spatial consistency is pivotal. The absolute 3D point accuracy especially varies within urban environments due to factors such as multipath and non-line-of-sight (NLOS) signal phenomena for GNSS receivers (Zhang et al, 2018, Lucks et al, 2021. Furthermore, the point clouds' unstructured nature and lack of semantics stand as challenging tasks for building reconstruction purposes (Xu and Stilla, 2021).…”