In this letter, we present a global navigation satellite system (GNSS) aided LiDAR-visual-inertial scheme, RailLoMer-V, for accurate and robust rail vehicle localization and mapping. RailLoMer-V is formulated atop a factor graph and consists of two subsystems: an odometer assisted LiDAR-inertial system (OLIS) and an odometer integrated Visual-inertial system (OVIS). Both the subsystem exploits the typical geometry structure on the railroads. The plane constraints from extracted rail tracks are used to complement the rotation and vertical errors in OLIS. Besides, the line features and vanishing points are leveraged to constrain rotation drifts in OVIS. The proposed framework is extensively evaluated on datasets over 800 km, gathered for more than a year on both general-speed and high-speed railways, day and night. Taking advantage of the tightly-coupled integration of all measurements from individual sensors, our framework is accurate to long-during tasks and robust enough to grievously degenerated scenarios (railway tunnels). In addition, the real-time performance can be achieved with an onboard computer.

Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required.

High-resolution remote sensing image change detection (CD) is one of the main methods to analyze land surface changes. How to effectively distinguish interesting changes and pseudochanges in high-resolution remote sensing images and form accurate and robust CD results is crucial. To deal with these problems, in this letter, we propose a deep-supervised dual discriminative metric network (SDMNet) trained end-to-end for CD in high-resolution bitemporal remote sensing images. A discriminative decoder network is designed in SDMNet to aggregate global and multiscale contextual information, utilizing high stage features to guide the selection of low stage features stage-by-stage to obtain more consistent and robust features. A discriminative implicit metric module is designed in SDMNet to measure the distance between features to detect changes and utilize batch-balanced contrastive loss (BCL) to enlarge the distance difference between unchanged pairs and changed pairs, while alleviate the problem of sample imbalance, and multiple change graph losses are introduced in the intermediate layer of the network for deep supervision. Quantitative evaluation of our method on the CD datasets SYSU, DSIFN, and CLCD demonstrates that the proposed method can provide superior performance than the other state-of-the-art methods.

Simultaneous Localization and Mapping (SLAM) technology is one of the best methods for fast 3D reconstruction and mapping. However, the accuracy of SLAM is not always high enough, which is currently the subject of much research interest. Panoramic vision can provide us with a wide range of angles of view, many feature points, and rich information. The panoramic multi-view cross-imaging feature can be used to realize instantaneous omnidirectional spatial information acquisition and improve the positioning accuracy of SLAM. In this study, we investigated panoramic visual SLAM positioning technology, including three core research points: (1) the spherical imaging model; (2) spherical image feature extraction and matching methods, including the Spherical Oriented FAST and Rotated BRIEF (SPHORB) and ternary scale-invariant feature transform (SIFT) algorithms; and (3) the panoramic visual SLAM algorithm. The experimental results show that the method of panoramic visual SLAM can improve the robustness and accuracy of a SLAM system.

Digital elevation model (DEM) interpolation is aimed at predicting the elevation values of unobserved locations, given a series of collected points. Over the years, the traditional interpolation methods have been widely used but can easily lead to accuracy degradation. In recent years, generative adversarial networks (GANs) have been proven to be more efficient than the traditional methods. However, the interpolation accuracy is not guaranteed. In this paper, we propose a GAN-based network named gated and symmetric-dilated U-net GAN (GSUGAN) for improved DEM interpolation, which performs visibly and quantitatively better than the traditional methods and the conditional encoder-decoder GAN (CEDGAN). We also discuss combinations of new techniques in the generator. This shows that the gated convolution and symmetric dilated convolution structure perform slightly better. Furthermore, based on the performance of the different methods, it was concluded that the Convolutional Neural Network (CNN)-based method has an advantage in the quantitative accuracy but the GAN-based method can obtain a better visual quality, especially in complex terrains. In summary, in this paper, we propose a GAN-based network for improved DEM interpolation and we further illustrate the GAN-based method’s performance compared to that of the CNN-based method.

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy.