Point Cloud Approach For Modelling The Lost Volume of The Fillaboa Bridge Cutwater

The digitisation of heritage is being rapidly realised in many parts of the world thanks to LiDAR technology. In addition to the simple digital preservation of heritage, 3D acquisition makes it possible to monitor the structural condition and assess possible damage. This paper presents a method for modelling the lost volume of a heritage bridge. The selected case study is the Fillaboa bridge, in Salvaterra de Miño, Spain, which has two cutwaters with the same cutting angle, one of which is damaged and has a stone loss. The bridge was acquired with a Terrestrial Laser Scanner. The method consists of the following processes. First, the walls of the whole cutwater are segmented and aligned by Iterative Closest Point algorithm over the damaged cutwater. Second, the distance between the two point clouds is calculated and the damaged area is delimited in both point clouds. And third, the alpha shape algorithm is applied to model the point cloud of the damaged area to a polygon. By searching for the optimal alpha radius, the polygon that best fits the damaged volume is generated. The proposed method also allows digital reconstruction of the damaged area, although it is sensitive to acquisition problems, which require manual interventions in the processing. The accuracy of the method is mainly dependent on the acquired point cloud registration (with an RMS error of 60mm) and the ICP registration error (31mm). Its use is limited to the existence of two geometries that allow superposition: one in good condition and one damaged to compare.

The Simultaneous Localization and Mapping (SLAM)-An Overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.

Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e

Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E.

VIGOROUS 3D ANGULAR RESECTION MODEL USING LEVENBERG – MARQUARDT METHOD

The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally solved using the least-squares method in an iterative approach. In this paper, we introduce a vigorous angular based resection method that converges to the global minimum even with very challenging starting values of the unknowns. The method is based on deriving oblique angles from the measured horizontal and vertical angles by solving spherical triangles. The derived oblique angles tightly connected the rays enclosed between the resection point and the reference points. Both techniques of the nonlinear least square adjustment either using the Gauss-Newton or Levenberg – Marquardt are applied in two 3D resection experiments. In both numerical methods, the results converged steadily to the global minimum using the proposed angular resection even with improper starting values. However, applying the Levenberg – Marquardt method proved to reach the global minimum solution in all the challenging situations and outperformed the Gauss-Newton method.

Targetless Coregistration of Terrestrial Laser Scanning Point Clouds Using a Multi Surrounding Scan Image-Based Technique

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are image-based and they are applied through projecting the point clouds back to the scanning stations. The projected 360 point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations.

Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.

Generation and Enrichment of Pedestrian Maps with Vertical Shadows in Urban Environments from Mobile Laser Scanning Data

Cities are becoming more pedestrian-friendly, reducing traffic and promoting physical activity and walking. However, prolonged exposure to the sun can cause sunburn and skin problems, so minimizing exposure to the sun while travelling is especially relevant at certain latitudes and in the summer months. This paper proposes a method for modelling urban contours and generating pedestrian maps with the location of shaded areas and accessibility barriers. The proposed method uses as input data a point cloud of an urban environment acquired with Mobile Laser Scanning. First, the input point cloud is segmented in ground points, obstacle points, and points causing shadows. Then, the three segmented point clouds are rasterized and the corresponded images are combined to obtain the navigable ground and the shaded areas. Finally, from the navigable ground, a navigation map is generated for pedestrians. To check the usefulness of this navigation map, a pathfinding algorithm is applied. The results show a correct generation of the navigable ground, and routes prioritizing the trajectory by shadow areas. Depending on the weighting between sun and shaded areas, the routes obtained show differences in distance travelled and sun exposure. The proposed method is sensitive to the existence of obstacles and noise in the point clouds.