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Indoor map is a fundamental element of indoor location-based services (ILBS). However, traditional indoor mapping techniques are labor-intensive and time-consuming. The advancement of smartphones offers great opportunities for crowdsourcing-based indoor mapping, which is one of the most promising applications due to its low cost and flexibility. Over the last decade, many crowdsourcing-based indoor mapping solutions using smartphones have been proposed. This article provides a systematic review of these works. Different from former surveys, we classify the indoor mapping process by the stage of map construction. In particular, we highlight the two key steps, geospatial-element acquisition, and indoor-map construction, and provide state-of-the-art techniques on these topics. Then, we systematically review the crowdsourcing-based indoor mapping solutions under grid-based, landmark-based, and semantic maps. In addition to covering the principles, benefits, and challenges, these systems are compared in terms of sensors, participation, output, experimental environment, and reported accuracy. Besides these existing performance criteria, we extract quantitative performance criteria that are suitable to evaluate crowdsourcing-based indoor mapping solutions. Finally, we present open issues and future research directions.

Improving the understanding of cloud–radiation–monsoon interactions is difficult due to the limited knowledge regarding the impacts of vertical cloud radiative forcing on monsoon circulation. Here,...

Defects and vertical short-wave irregularities significantly influence the maintenance of the railway tracks and the train safety, and hence, they need to be detected and eliminated in the early stages of formation. We studied the principles and algorithms to detect the defects and to measure the vertical short-wave irregularities by using a fiber optical gyroscope and an odometer, and the associated software was also developed. The principles of deformation index, namely, the ratio of angular rate to velocity and the method to determine the threshold of defects detection, are expatiated. The influence of the transfer function in measuring the vertical irregularities has long been a tough issue. The relation between the characteristics of deformation index and the wavelength of irregularities is studied by converting the deformation index from the time domain to the spatial domain. The post-processing module utilizes the results of the spectrum analysis of the deformation index to determine the wavelength of the track irregularities and eliminate the influence of the transfer function. To improve the accuracy of the calculation results, we analyzed the characteristics of the output of the gyroscope and the odometer and designed a series of targeted error-elimination methods. The forward and reverse dead-reckoning method is utilized to reduce the error accumulation, especially a high frequency noise when calculating the magnitude of the vertical short-wave irregularities. The experimental results show that the repeatability is 0.038 mm when the chord length is 1 m and 0.65 mm when the chord length is 10 m. The accuracy is 0.05 mm when compared with the results obtained using a ruler of 1 m. The moving speed of the instrument was approximately 0.6-1.2 m/s during the experiment. Both the accuracy and the efficiency can fulfill the requirements for the measurement of the short-wave irregularities.

Ocean waves are a vital environmental factor that affects the accuracy of airborne laser bathymetry (ALB) systems. As the regional water surface undulates with randomness, the laser propagation direction through the air–water surface will change and impact the underwater topographic result from the ALB system, especially for the small laser divergence system. However, the natural ocean surface changes rapidly over time, and uneven ocean surface point clouds from ALB scanning will cause an uncertain estimation of the laser propagation direction; therefore, a self-adaptive correction method based on the characteristics of the partial wave surface is key to improving the accuracy and applicability of the ALB system. In this paper, we focused on the issues of spatial position deviation caused by surface waves and position correction of the underwater laser footprint, and the dimension-based adaptive method is applied to attempt to correct the laser incidence angle. Simulation experiments and analysis of the actual measurement data from different ALB systems verified that the method can effectively suppress the influence of ocean waves. Furthermore, the inversion result of sea surface inclination changes is consistent with the surface wind wave reanalysis products. Based on the laser underwater propagation model in the strategy, we also quantitatively analyzed the influence of surface waves on laser bathymetry, which can guide the operation selection and data processing of the ALB system at specific water depths and under dynamic ocean conditions.

The inertial navigation system (INS)/odometer integrated dead reckoning (DR) is usually used for the positioning and orientation of mobile LiDAR system in the precision surveying of railway tunnels. However, due to the accumulation of pose errors and the creaky method of joint surveying of track control points, it is difficult to achieve the millimeter level absolute accuracy of point clouds relative to the track control network under dynamic conditions, especially when there are gross errors of some control points coordinates. This paper puts forward an algorithm to optimize accuracy of INS/odometer integrated DR with robust constraints of track control network. The errors of DR are divided into two types, which affect the relative accuracy and the absolute accuracy respectively; The concept of virtual mileage coordinate system is proposed, in this coordinate system, the influence of gyro bias error on position is modeled as a circular distribution and estimated indirectly by using the high-precision relative spatial relationship of control points; An absolute accuracy optimization method with constraints by sequence match between positions of surveying equipment and track control points is proposed, and the gross errors of control point coordinates are detected and processed based on reduced weight iteration method. The experimental results of North China high speed railway tunnel show that when the distance interval of control points of correction is 120 m, the root mean square (RMS) error of coordinates in X, Y and Z directions are 0.003, 0.003 and 0.005 m respectively. When the distance interval of control points of correction is 420 m, the RMS in X, Y and Z directions are 0.029, 0.030 and 0.010 m respectively. When there are gross errors of some control point coordinates and the distance interval of control points of correction is 120 m, the RMS of coordinates in X, Y and Z directions are 0.005, 0.006 and 0.006 m respectively, and the gross errors of 4 cm in one direction of the three-dimensional control point coordinates can be detected. The experimental results verified the high accuracy of the algorithm and its robustness to the gross errors of control point coordinates.

When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short, however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS&rsquo, s dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing&ndash, Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5&ndash, 6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.

Indoor mapping is an essential element of indoor navigation systems. In this paper, we present a graph optimization-based method for indoor map construction, which can be used in buildings without prior knowledge. By using crowdsourcing data on mobile phone sensors, we can derive activity landmarks where people perform different activities (turning, taking the elevator, taking the escalator, and walking up/down the stairs). Our method uses graph optimization techniques to align crowdsourcing trajectories by their intersecting landmarks. After trajectory alignment, this method applies a pose graph optimization method to construct an indoor map. Finally, our method performs a transform from relative coordinates to an absolute coordinate with reference points and reduces the redundant segments using dynamic time warping. To evaluate the performance, we implement the proposed method in an office building and a shopping mall. Experiment results show that 80th percentile error of the mapping accuracy is about 1.7-3.5 m. Moreover, the proposed method can deal with the curved routes in a building and can also decrease the amount of required data.

Underground cavity three-dimensional measurements constitute an important and challenging task in underground mining. The laser scanning method is a relatively precise and efficient way to address this problem, but it is limited by the unevenness of the point cloud that reduces the scanning efficiency. In this paper, an approach that can increase the evenness of the point cloud in underground laser scanning is proposed. The approach is based on a spiral trajectory planning strategy. The scanning trajectory is generated from previous scan by solid geometry. Exceptions are discussed and solutions are proposed. The approach was used in an actual laser scanning system and experiments were carried out in a gold mine in Hebei Province, North China. To assess the results, 3D Delaunay triangulation network of laser points was built in accordance to a semispherical-planer projection rule, and the area of each triangle in the network was calculated. The coefficient of variance of the triangular area indicated that the new approach can increase the evenness of the laser points efficiently.

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

Purpose The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes. Design/methodology/approach In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency. Findings The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes. Originality/value This paper presents a method that improves the image-based visual saliency model.

Providing drivers with speed advisories is an effective eco-driving method at signalized intersections. However, all current research on speed advisory models has excluded the driver's behavior factor. In this paper, we focus on developing a speed advisory model that is able to adapt to the driver's behavior for eco-driving. First, we propose a closed-loop speed advisory framework, with simulation results to show that the current model could not fit in the closed-loop implementation. Next, the continuous acceleration with explicit high velocity boundary (CAEHV) model is established to address the issues when the existing model is used. However, the simulation results for the CAEHV model are not fully satisfactory due to the existence of oscillations in actual speed trajectories. Third, the CAEHV with coasting (CAEHV-C) model is established, in which the vehicle coasting is applied to supplement cruising to avoid oscillations. Simulation results show that the fuel economy performance of the CAEHV-C model is improved by 4% when compared with the CAEHV model. It also shows that CAEHV-C performs the best in terms of the driver's behavior adaptability.

With the increase in the number of service years for high-speed railways, the foundation of the rail track suffers from settlement, which causes rail track irregularity. To adjust the position of the track and meet track regularity demands, several components of the fastening system will be replaced by different sized components. It is important to measure the exact geometric parameters for the components of a fastening system before adjusting the track. Currently, the measurement process is conducted manually, which is laborious and error-prone. In this paper, a real-time geometric parameter measurement system for high-speed railway fastener based on 2-D laser profilers is presented. Dense and precise 3-D point clouds of high-speed railway fasteners are obtained from the system. A fastener extraction method is presented to extract fastener point cloud and a region-growing algorithm is used to locate key components of the fastener. Then, the geometric parameter of the fastener is worked out. An experiment was conducted on a high-speed railway near Wuhan, China to verify the accuracy and repeatability of the system. The maximum root-mean-square-error between the manual measurement and the system measurement is 0.3 mm, which demonstrates adequate accuracy. This system can replace manual measurements and greatly improve the efficiency of geometric parameter measurements for fasteners.

High-precision indoor three-dimensional maps are a prerequisite for building information models, indoor location-based services, etc., but the indoor mapping solution is still in the stage of technological experiment and application scenario development. In this paper, indoor mapping equipment integrating a three-axis laser scanner and panoramic camera is designed, and the corresponding workflow and critical technologies are described. First, hardware design and software for controlling the operations and calibration of the spatial relationship between sensors are completed. Then, the trajectory of the carrier is evaluated by a simultaneous location and mapping framework, which includes reckoning of the real-time position and attitude of the carrier by a filter fusing the horizontally placed laser scanner data and inertial measurement data, as well as the global optimization by a closed-loop adjustment using a graph optimization algorithm. Finally, the 3D point clouds and panoramic images of the scene are reconstructed from two tilt-mounted laser scanners and the panoramic camera by synchronization of the position and attitude of the carrier. The experiment was carried out in a five-story library using the proposed prototype system; the results demonstrate accuracies of up to 3 cm for 2D maps, and up to 5 cm for 3D maps, and the produced point clouds and panoramic images can be utilized for modeling and further works related to large-scale indoor scenes. Therefore, the proposed system is an efficient and accurate solution for indoor 3D mapping.

Metasurfaces consist of dielectric nanobrick arrays with different dimensions in the long and short axes can be used to generate different phase delays, predicting a new way to manipulate an incident beam in the two orthogonal directions separately. Here we demonstrate the concept of depth perception based three-dimensional (3D) holograms with polarization-independent metasurfaces. 4-step dielectric metasurfaces-based fan-out optical elements and holograms operating at 658 nm were designed and simulated. Two different holographic images with high fidelity were generated at the same plane in the far field for different polarization states. One can observe the 3D effect of target objects with polarized glasses. With the advantages of ultracompactness, flexibility and replicability, the polarization-independent metasurfaces open up depth perception based stereoscopic imaging in a holographic way.

This paper presents an activity sequence-based indoor pedestrian localization approach using smartphones. The activity sequence consists of several continuous activities during the walking process, such as turning at a corner, taking the elevator, taking the escalator, and walking stairs. These activities take place when a user walks at some special points in the building, like corners, elevators, escalators, and stairs. The special points form an indoor road network. In our approach, we first detect the user's activities using the built-in sensors in a smartphone. The detected activities constitute the activity sequence. Meanwhile, the user's trajectory is reckoned by Pedestrian Dead Reckoning (PDR). Based on the detected activity sequence and reckoned trajectory, we realize pedestrian localization by matching them to the indoor road network using a Hidden Markov Model. After encountering several special points, the location of the user would converge on the true one. We evaluate our proposed approach using smartphones in two buildings: an office building and a shopping mall. The results show that the proposed approach can realize autonomous pedestrian localization even without knowing the initial point in the environments. The mean offline localization error is about 1.3 m. The results also demonstrate that the proposed approach is robust to activity detection error and PDR estimation error.

Indoor maps are integral to pedestrian navigation systems, an essential element of intelligent transportation systems (ITS). In this paper, we propose ALIMC, i.e., Activity Landmark-based Indoor Mapping system via Crowdsourcing. ALIMC can automatically construct indoor maps for anonymous buildings without any prior knowledge using crowdsourcing data collected by smartphones. ALIMC abstracts the indoor map using a link–node model in which the pathways are the links and the intersections of the pathways are the nodes, such as corners, elevators, and stairs. When passing through the nodes, pedestrians do the corresponding activities, which are detected by smartphones. After activity detection, ALIMC extracts the activity landmarks from the crowdsourcing data and clusters the activity landmarks into different clusters, each of which is treated as a node of the indoor map. ALIMC then estimates the relative distances between all the nodes and obtains a distance matrix. Based on the distance matrix, ALIMC generates a relative indoor map using the multidimensional scaling technique. Finally, ALIMC converts the relative indoor map into an absolute one based on several reference points. To evaluate ALIMC, we implement ALIMC in an office building. Experiment results show that the 80th percentile error of the mapping accuracy is about 0.8–1.5 m.

In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature value computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.

Rail surface defects such as the abrasion, scratch and peeling often cause damages to the train wheels and rail bearings. An efficient and accurate detection of rail defects is of vital importance for the safety of railway transportation. In the past few decades, automatic rail defect detection has been studied; however, most developed methods use optic-imaging techniques to collect the rail surface data and are still suffering from a high false recognition rate. In this paper, a novel 3D laser profiling system (3D-LPS) is proposed, which integrates a laser scanner, odometer, inertial measurement unit (IMU) and global position system (GPS) to capture the rail surface profile data. For automatic defect detection, first, the deviation between the measured profile and a standard rail model profile is computed for each laser-imaging profile, and the points with large deviations are marked as candidate defect points. Specifically, an adaptive iterative closest point (AICP) algorithm is proposed to register the point sets of the measured profile with the standard rail model profile, and the registration precision is improved to the sub-millimeter level. Second, all of the measured profiles are combined together to form the rail surface through a high-precision positioning process with the IMU, odometer and GPS data. Third, the candidate defect points are merged into candidate defect regions using the K-means clustering. At last, the candidate defect regions are classified by a decision tree classifier. Experimental results demonstrate the effectiveness of the proposed laser-profiling system in rail surface defect detection and classification.

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

Recent inpainting techniques usually require human interactions which are labor intensive and dependent on the user experiences. In this paper, we introduce an automatic inpainting technique to remove undesired fence-like structures from images. Specifically, the proposed technique works on the RGBD images which have recently become cheaper and easier to obtain using the Microsoft Kinect. The basic idea is to segment and remove the undesired fence-like structures by using both depth and color information, and then adapt an existing inpainting algorithm to fill the holes resulting from the structure removal. We found that it is difficult to achieve a satisfactory segmentation of such structures by only using the depth channel. In this paper, we use the depth information to help identify a set of foreground and background strokes, with which we apply a graph-cut algorithm on the color channels to obtain a more accurate segmentation for inpainting. We demonstrate the effectiveness of the proposed technique by experiments on a set of Kinect images.

This paper proposes a high-efficiency and continuous levelling method for linear objects, such as highways and railways, by applying a vehicle-borne GPS/INS kinematic surveying system which integrates the Global Positioning System (GPS) and the Inertial Navigation System (INS). Generally, the raw ellipsoid height directly outputted by GPS/INS needs to be transformed to the local orthometric height. However, because of various systematic errors and random errors, such as the measuring error and the quasi-geoid model error, the accuracy of the orthometric height would rarely meet engineering requirements. To solve this problem, a novel sliding least-squares collocation (SLSC) model based on ground control points was constructed to improve the accuracy of GPS/INS kinematic levelling. Two data sets collected from the Chang Ying highway located in Jilin province, China, were used to validate the proposed method. The experimental results indicate that the proposed method improves the trajectory points’ orthometric height accuracy by at least 160%.

Shield subway tunnels are critical urban infrastructure and precise, rapid deformation detection method is required for the maintenance of these tunnels. A tunnel deformation detection system called Railway Mobile Measurement System (RMMS) was developed based on mobile laser scanning technique. Herein, detailed analysis is given to prove that profiles from the laser scanner can be seen as cross-sections of the tunnel and that the corresponding error can be ignored. A wavelet filtering algorithm was developed to filter the point cloud of tunnel ancillary facilities in the cross-section points. A subsequent ellipse fitting and deformation detection method is proposed. A cross-section location method for subway tunnels is also presented based on the greyscale image converted from the tunnel's point cloud. A tunnel ring segments seams detection method is proposed to detect tunnel ring segments dislocation. The accuracy of the system was verified in a subway tunnel in Wuhan, China with a total station. The accuracy of cross-section measurement was 1.5 mm, the repeatability of the overall and local deformation measurement was 0.5 and 0.9 mm, respectively. This system completely fulfils the accuracy requirement of railway tunnels deformation detection and its efficiency is significantly higher than that of the traditional method.