Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.1616

Method for automatic railway track surface defect classification and evaluation using a laser-based 3D model

Due to the unique and contactless way of identification, radio-frequency identification is becoming an emerging technology for objects tracking. As radio-frequency identification does not provide a...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719860990

Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder:

Laser scanner takes away the problem of private life conservation as it does not record real world videos except scanned data points. So it shows many benefits over the use of video camera. This paper portrays an approach to detect and track movers from laser scanned datasets. Laser scanned data points from each scan are deemed as a video frame. Blobs are extracted from each frame. Support vector machine (SVM) and Hungarian method along with particle filter are used to get trajectories of movers. Experimental results on the identical laser scanned dataset demonstrate that the approach of SVM with Hungarian method using particle filter outperforms both the threshold based approach with Hungarian method using Kalman filter and the approach of SVM with Hungarian method using Kalman filter.

Particle Filter for Trajectories of Movers from Laser Scanned Dataset

Two dimensional (2D) laser rangefinders (LRF) are widely utilized in various mobilized systems, such as robot, intelligent vehicles (IVs), and mobile mapping. Pose estimation for a 2D LRF has important applications for exterior calibration of multiple sensors, registering light detection and ranging (LIDAR) data from multiple LRFs or from a spinning/nodding LRF, etc. This paper shows that the pose of a 2D LRF can be uniquely determined from the single-shot of a trirectangular trihedron. The pose is estimated by solving a simplified Perspective-Three-Point (P3P) problem and a Three-Point-Registration problem. Compared to the raw noisy LIDAR data, the three control points in P3P problem are estimated from line fitting of the sensed LIDAR data to guarantee good and reliable estimation results. The proposed method has been validated with both simulation and real data. The results show that the method is accurate and practical. To the best of our knowledge, this paper solves the pose estimation of a 2D LRF by using LIDAR data of one scan for the first time.

A single-shot pose estimation approach for a 2D laser rangefinder

The accuracy of terrestrial 3D laser scanners is mainly determined by the accuracy of ranging and angle measurement. Therefore, the research of horizontal angle accuracy is very important. The current construction industry can develop at the fastest speed because of the advent of many advanced science and technology research and development. The rational application of these science and technology to the construction industry can intuitively display the internal structure and external shape of the building. In front of people, it effectively guarantees the country's economic benefits and the people's property safety. The article studies the influence of distance, incident angle and target color on the accuracy of the scanner during measurement. Using the plane fitting method to analyse the accuracy, a qualitative analysis conclusion is drawn that as t he distance increases, the angle of incidence increases, which will cause the measurement accuracy of the ground 3D laser scanner to decrease.

Research on Building Measurement Accuracy Verification Based on Terrestrial 3D Laser Scanner

Estimating the three-dimensional (3D) motion from sparse laser point clouds is a highly challenging endeavour facing computer and robotic vision engineers. In this study, a novel method is proposed for robustly estimating the scene flow from a laser scanner assisted by a camera. Conditional random field (CRF) is constructed by a spatial structure of point clouds, the energy of which is minimised by a synchronous calibrated image. With the high frame rate of a laser scanner, the authors' method allows for estimating the potential motion field as the CRF label. The authors ran an experiment on a public dataset to demonstrate that their method can accurately estimate rigid motion in outdoor scenes. They also tested the method on a laser scanner and omni-directional camera system to find that it also accurately estimates the rigid and semi-rigid motion of objects in a controlled indoor environment.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2017.0876

Scene flow for 3D laser scanner and camera system

The vision-based mobile robot's simultaneous localisation and mapping and navigation capability in dynamic environments are highly problematic elements of robot vision applications. The goal of this study is to reconstruct a static map and track the dynamic object for a camera and laser scanner system. An improved automatic calibration is designed to merge image and laser point clouds. Then, the fusion data is exploited to detect the slowly moved object and reconstruct static map. Tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. In the proposed method, occluded regions are combined 3D motion models with object appearance to manage difficulties in crowded scenes. The proposed method was validated by experimental results gathered in a real environment and on publicly available data.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2017.0308

Liwei Zhang

Papers

Scene flow for 3D laser scanner and camera system

Static map reconstruction and dynamic object tracking for a camera and laser scanner system