Improving GPS Trajectories Using 3d City Models and Kinematic Point Clouds

Abstract

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

Smart Material Handling Solutions for City Logistics Systems

“…Bearing in mind the numerous analyzed limitations, the overcoming of which would increase the efficiency of customer service, the main goal of this paper is the selection of smart solutions for MH based on combinations of modern MH equipment and I 4.0 technology. In order to overcome some key challenges, four alternatives are discussed in this research: SAGV forklifts, AMRs, GTP and UAVs, which are supported by CWMS, CGPS, BD, DM, IIoT, AI and AR as the most significant I 4.0 technologies for MH activities [81][82][83][84][85][86][87][88][89][95][96][97][98][99][100][101][102][103][104][105][106][107][108]. Each alternative is analyzed in terms of its advantages and disadvantages from the perspective of MH in logistics center and are evaluated based on fourteen criteria.…”

“…IIoT sensors record and update information on warehouse operations. The routing and navigational functions of smart MH equipment depend on CGPS [83,84]. The system that integrates and monitors all warehouse operations is known as CWMS [85,86].…”

“…With the application of AI technology, UAVs can be operated automatically and without the need for direct human supervision. Navigation and management are performed using CGPS systems and IIoT sensors that provide the necessary data on the positions of equipment and drones [79,83]. CWMS is necessary for the communication of all MH and other equipment and goods with the central computer.…”

Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

“…In this case, the georeferencing is referred to as "data-driven". The referenced datasets, in this case, could be 3D point clouds [17], digital surface models, or 3D city models [7,18,19].…”

Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

“…In addition to control points, area-based evaluation methods are widespread. In this respect, existing 3D city models can be utilized as a reference for evaluation [60][61][62]. Furthermore, reference point clouds (e.g., from TLS) are commonly used for evaluation (e.g., [10,11,18,21,33,[63][64][65]), since software packages provide algorithms for the comparison of two point clouds.…”