Over the past years a lot of effort is being focused on realizing the vision of fully autonomously driving vehicles. The achievement of this goal strongly depends on the development of sensors that allow the perception of the environment by scanning it with high speed, precision and resolution. The sensors employed in autonomous vehicles typically comprise cameras, Radar and LiDAR Systems. Especially LiDAR and Camera Sensors deliver the necessary high-resolution data, but both suffer from strongly degrading signals in low visibility conditions. To guarantee the safe operation of autonomously driving vehicles the existing sensors need to be improved with respect to these conditions and new sensors need to be developed. In this contribution we present a LiDAR system design that is optimized for the operation in low visibility conditions. On one hand we address the technical details of the system such as choice of laser, detector, deflection unit and signal processing electronics. Besides the technical details of the system, we discuss the physical and technological limitations such as wavelength dependent scattering and absorption and eye safety considerations. We further give an outlook on a sensor fusion approach with a time-gated sensor with high lateral resolution for a better recognition of objects obscured by scattering media.
Tunnel inspections require the detection of deformations in the tunnel geometry, cracks, delamination, and water inflow. Solutions for an automated detection of deformations, cracks and water inflow already exist and typically comprise mobile laser-scanners and cameras combined with deep learning methods. Delaminations on the other hand are often not visible on the surface and can’t be detected using these methods. The detection of delamination in tunnel linings is therefore up to date performed by manual hammering and acoustic detection. The results are time consuming and labor-intensive inspections, subjective measurements, poor comparisons over epochs and a low degree of digitization. We present a concept of a novel system that aims to replace the manual hammering for acoustic delamination detection using a remote sensing approach. A strong, pulsed laser serves as a hammer and creates a plasma induced shockwave on the concrete surface. If a delamination is present this shockwave excites characteristic, resonant vibrations. A second, narrow-linewidth laser is employed in a customized laser doppler vibrometer setup to remotely detect these vibrations via a coherent measurement technique. In combination with laser scanners and cameras, the laser based remote sensing technique has the potential to help automating the process of tunnel inspections by delivering objective data that can be used in deep learning-based evaluation methods and for building information modeling (BIM) compliant assessment. A first mobile prototype for measurements outside the lab has been developed and is being presented in detail.
In this paper, we present the implementation of a differential-absorption measurement-technique for surface moisture detection into a laser scanner aiding modern tunnel inspections. The use of laser scanners for tunnel inspections can reduce costly tunnel closures and provide digital data, compliant with modern building information modeling (BIM). Unfortunately, available systems are typically limited to pure 3D mapping. The Fraunhofer Institute for Physical Measurement Techniques IPM is developing a novel multi-parameter laser scanning system. For the first time, this system allows the simultaneous measurement of 3D-geometry, remission and surface moisture. The scanner measures simultaneously with two collinear laser beams with distinct wavelengths. One is centered at the absorption band of water at 1450 nm wavelength, while the other, with 1320 nm wavelength, is used as an intensity reference. The intensity ratio gives a good estimate of the surface water content. Additionally, the power of both lasers is modulated with a high frequency. This enables simultaneous measurement of the distance by comparing the phase difference of the backscattered light with a local reference. With this approach, we are able to record up to two million points per second containing distance, intensity and moisture information. Besides the technical implementation, we present point clouds from multiple test objects and surfaces. The presented data nicely demonstrates the ability to differentiate between absolute intensity variations, e.g. caused by dirt, and actual water contamination.
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