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AeroInspekt

AeroInspekt Header

Automated Measurement of Rail Infrastructure in the Hamburg Harbour

In the CTA terminal run by the Hamburger Hafen und Logistik AG (HHLA), containers are transshipped mostly automated. The rails used by the automatic gantry cranes must be checked regularly for deformations and measured with millimeter precision. The current methods to do this are cost intensive as the cranes have to be stopped for several hours.

AeroInspekt aims to measure the rails using a multicopter without interfering the operation of the terminal. The drone is taking highly overlapping aerial images of the rail areas and calculate a three-dimensional model out of them by photogrammetry.

Overview of the automatic terminal CTA
Overview of the automatic terminal CTA

Automated reactive flight planning

To measure the rails with millimeter precision the flight system takes aerial images with a ground resolution of under 1 mm. To map the whole measurement area, unobstructed images of the rails are needed. Significant parts of the rails will normally be covered by the operating cranes, leading to a loss of relevant information in the mapping result.

The ground control app Inspekt GS developped in AeroInspekt solves this problem with the implementation of an automated reactive flight planning function. It recognizes relevant obstrucctions with a machine learning approach while still in flight. The accordingly marked sections are covered again until sufficient data is gathered.

DJI M600 über dem Vermessungsgebiet

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Automated Analysis

To connect the three-dimensional data to the harbour reference system with millimeter precision, specifically designed hybrid markers are installed in the track bed. With a combination of prisms and machine-processable optical markers these can be both measured from the ground with a total station and recognized automatically in the aerial images. After the flight, the raw data is processed with a photogrammetry workflow to create a three-dimensional point cloud.

To extract the rails position from the point cloud, we created the program Inspekt XT. It detects the rails profile in periodically generated two-dimensional slices and calculateds the rails position. The issued measurement report can by used by the responsible authorities to recognize a breach of thresholds that might disturb the operation and take appropriate actions.

DJI Matrice 600 Pro on a survey mission over the gantry cranes in 35 meters height
DJI Matrice 600 Pro on a survey mission over the gantry cranes in 35 meters height
Hybrid markers in the track bed (right) and automated detection in the aerial image (left)
Hybrid markers in the track bed (right) and automated detection in the aerial image (left)
Dense point cloud of the measurement area (2.5 million points per square meter)
Dense point cloud of the measurement area (2.5 million points per square meter)
Automatic extraction of the rails position from the point cloud by analysis of profile slices
Automatic extraction of the rails position from the point cloud by analysis of profile slices
Details on crane detection

The detection of conflicts (in the form of obstructions) can be done based on one of the following data sources:

Computer Vision: A 4-layered convolutional neural net with RELU-activations was trained on a dataset of around 113.000 images. It runs inside the Inspekt GS app on an Android tablet and reaches an accuracy of 96%.

GPS-Boxes: Independent interconnected boxes can be installed on the cranes as a reference. They constantly send the crane's position to Inspekt GS and allow an even more reliable crane detection.

Manual detection: For a flexible application, the detection can be done by the operator directly in Inspekt GS. Automatic detections can be overwritten at any time.

K1: Rails blocked by crane
K1: Rails blocked by crane
K2: Crane detection with computer vision
K2: Crane detection with computer vision
K3: GPS-Box for crane localization
K3: GPS-Box for crane localization
Details on the reached accuracy

The accuracy of the data in the global system is mainly dependent on the accuracy of the measurement of the reference points. Currently these can be measured with a RMSE of 3 mm. Figure G1 shows the deviations at the distributed reference points and control points. At the control points results a RMSE of 3 mm.

Figure G3 compares the rail position in the horizntal plane extracted by Inspekt XT and the classic rals measurements. The RMSE is again 3 mm . Figure G4 shows the vertical deviation. Here, the RMSE is 7 mm.

Over the course of the project, these deviations are expected be reduced by at least a half.

G1: Deviations at ground reference points (GCP) and control points (CP) after photogrammetry
G1: Deviations at ground reference points (GCP) and control points (CP) after photogrammetry
G2: Extracted rail edges and center line with reference (H&P)
G2: Extracted rail edges and center line with reference (H&P)
G3: Deviation of the extraced rail position (XY) to the ideal position compared to the reference (H&P). The reference has a comparable accuracy level
G3: Deviation of the extraced rail position (XY) to the ideal position compared to the reference (H&P). The reference has a comparable accuracy level.
G4: Deviation of the extraced rail position (Z) to the ideal position compared to the reference (H&P). The reference has a comparable accuracy level.
G4: Deviation of the extraced rail position (Z) to the ideal position compared to the reference (H&P). The reference has a comparable accuracy level.

Project AeroInspekt
  Automated Multirotor Measurement and Inspection System
Funding Period August 2018 - October 2020
Cooperation partners Hamburger Hafen und Logistik AG (HHLA)
  Institute of Flight Guidance (IFF)
  Institut of Geodesy and Photogrammetry (IGP)
Funding Institution BMVI - IHATEC
Funding Code 19H18006B
Contacts Yogesh Khedar (IFF)
  Yahya Ghassoun (IGP)
  Christina Mügge (HHLA)
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