Search

Railway tunnel clearance is directly related to the safe operation of trains and upgrading of freight capacity. As more and more railway are put into operation and the operation is continuously becoming faster, the railway tunnel clearance inspection should be more precise and efficient. In view of the problems existing in traditional tunnel clearance inspection methods, such as low density, slow speed and a lot of manual operations, this paper proposes a tunnel clearance inspection approach based on 3D point clouds obtained by a mobile laser scanning system (MLS). First, a dynamic coordinate system for railway tunnel clearance inspection has been proposed. A rail line extraction algorithm based on 3D linear fitting is implemented from the segmented point cloud to establish a dynamic clearance coordinate system. Second, a method to seamlessly connect all rail segments based on the railway clearance restrictions, and a seamless rail alignment is formed sequentially from the middle tunnel section to both ends. Finally, based on the rail alignment and the track clearance coordinate system, different types of clearance frames are introduced for intrusion operation with the tunnel section to realize the tunnel clearance inspection. By taking the Shuanghekou Tunnel of the Chengdu-Kunming Railway as an example, when the clearance inspection is carried out by the method mentioned herein, its precision can reach 0.03 m, and difference types of clearances can be effectively calculated. This method has a wide application prospects. [ABSTRACT FROM AUTHOR]

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping. [ABSTRACT FROM AUTHOR]

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. [ABSTRACT FROM AUTHOR]