Documentation of Complex Environments Using 360° Cameras. The Santa Marta Belltower in Montanaro

Low-cost and fast surveying approaches are increasingly being deployed in several domains, including in the field of built heritage documentation. In parallel with mobile mapping systems, uncrewed aerial systems, and simultaneous location and mapping systems, 360° cameras and spherical photogrammetry are research topics attracting significant interest for this kind of application. Although several instruments and techniques can be considered to be consolidated approaches in the documentation processes, the research presented in this manuscript is focused on a series of tests and analyses using 360° cameras for the 3D metric documentation of a complex environment, applied to the case study of a XVIII century belltower in Piemonte region (north-west Italy). Both data acquisition and data processing phases were thoroughly investigated and several processing strategies were planned, carried out, and evaluated. Data derived from consolidated 3D mapping approaches were used as a ground reference to validate the results derived from the spherical photogrammetry approach. The outcomes of this research confirmed, under specific conditions and with a proper setup, the possibility of using 360° images in a Structure from Motion pipeline to meet the expected accuracies of typical architectural large-scale drawings.

Integration of a Wearable Mobile Mapping Solution and Advance Numerical Simulations for the Structural Analysis of Historical Constructions: A Case of Study in San Pedro Church (Palencia, Spain)

This work aims at enhancing the current methodologies used for generating as-built CAD models suitable for advanced numerical simulations. To this end, this paper proposes the use of a wearable mobile mapping system that allows one to improve the digitalization stage in terms of flexibility and time required. The noise showed by the resulting point cloud, based on the simultaneous location and mapping (SLAM) solution, demands a post-processing stage that introduces the use of a parameter-free noise reduction filter. This filter improves the quality of the point cloud, allowing for the adjustment of surfaces by means of parametric and non-parametric shapes. These shapes are created by using reverse engineering procedures. The results showed during this investigation highlight a novel application of this sensor: the creation of as-built CAD models for advanced numerical simulations. The results of this investigation are complemented by a valuable contribution with respect to the use of an advanced restoration solution, by means of textile reinforced mortar. To this end, the CAD model is used as the geometrical base for several numerical simulations by means of the finite element method. All this procedure is applied in a construction with structural problems.

Microdecisions and autonomy in self-driving cars: virtual probabilities

To operate in an unpredictable environment, a vehicle with advanced driving assistance systems, such as a robot or a drone, not only needs to register its surroundings but also to combine data from different sensors into a world model, for which it employs filter algorithms. Such world models, as this article argues with reference to the SLAM problem (simultaneous location and mapping) in robotics, consist of nothing other than probabilities about states and events arising in the environment. The model, thus, contains a virtuality of possible worlds that are the basis for adaptive behavior. The article shows that the current development of these technologies requires new concepts because their complex adaptive behaviors cannot be explained by referring them to mere algorithmic processes. Instead, it proposes the heuristic instrument of microdecisions to designate the temporality of decisions between alternatives that are created by probabilistic procedures of world modeling. Microdecisions are more than the implementation of deterministic processes—they decide between possibilities and, thus, always open up the potential of their otherness. By describing autonomous adaptive technologies with this heuristic, the question of sovereignty inevitably arises. It forces us to re-think what autonomy means when decisions can be automated.

Navigation and Mapping in Forest Environment Using Sparse Point Clouds

Odometry during forest operations is demanding, involving limited field of vision (FOV), back-and-forth work cycle movements, and occasional close obstacles, which create problems for state-of-the-art systems. We propose a two-phase on-board process, where tree stem registration produces a sparse point cloud (PC) which is then used for simultaneous location and mapping (SLAM). A field test was carried out using a harvester with a laser scanner and a global navigation satellite system (GNSS) performing forest thinning over a 520 m strip route. Two SLAM methods are used: The proposed sparse SLAM (sSLAM) and a standard method, LeGO-LOAM (LLOAM). A generic SLAM post-processing method is presented, which improves the odometric accuracy with a small additional processing cost. The sSLAM method uses only tree stem centers, reducing the allocated memory to approximately 1% of the total PC size. Odometry and mapping comparisons between sSLAM and LLOAM are presented. Both methods show 85% agreement in registration within 15 m of the strip road and odometric accuracy of 0.5 m per 100 m. Accuracy is evaluated by comparing the harvester location derived through odometry to locations collected by a GNSS receiver mounted on the harvester.

Illumination Robust Loop Closure Detection with the Constraint of Pose

Background: Loop closure detection is a crucial part in robot navigation and simultaneous location and mapping (SLAM). Appearance-based loop closure detection still faces many challenges, such as illumination changes, perceptual aliasing and increasing computational complexity. Methods: In this paper, we proposed a visual loop closure detection algorithm that combines illumination robust descriptor DIRD and odometry information. In this algorithm, a new distance function is built by fusing the Euclidean distance function and Mahalanobis distance function, which integrates the pose uncertainty of body and can dynamically adjust the threshold of potential loop closure locations. Then, potential locations are verified by calculating the similarity of DIRD descriptors. Results: The proposed algorithm is evaluated on KITTI and EuRoC datasets, and is compared with SeqSLAM algorithm, which is one of the state of the art loop closure detection algorithms. The results show that the proposed algorithm could effectively reduce the computing time and get better performance on P-R curve. Conclusion: The new loop closure detection method makes full use of odometry information and image appearance information. The application of the new distance function can effectively reduce the missed detection caused by odometry error accumulation. The algorithm does not require extracting image features or learning stage, and can realize real-time detection and run on the platform with limited computational power.

Accumulative Errors Optimization for Visual Odometry of ORB-SLAM2 Based on RGB-D Cameras

Oriented feature from the accelerated segment test (oFAST) and rotated binary robust independent elementary features (rBRIEF) SLAM2 (ORB-SLAM2) represent a recognized complete visual simultaneous location and mapping (SLAM) framework with visual odometry as one of its core components. Given the accumulated error problem with RGB-Depth ORB-SLAM2 visual odometry, which causes a loss of camera tracking and trajectory drift, we created and implemented an improved visual odometry method to optimize the cumulative error. First, this paper proposes an adaptive threshold oFAST algorithm to extract feature points from images and rBRIEF is used to describe the feature points. Then, the fast library for approximate nearest neighbors strategy is used for image rough matching, the results of which are optimized by progressive sample consensus. The image matching precision is further improved by using an epipolar line constraint based on the essential matrix. Finally, the efficient Perspective-n-Point method is used to estimate the camera pose and a least-squares optimization problem is constructed to adjust the estimated value to obtain the final camera pose. The experimental results show that the proposed method has better robustness, higher image matching accuracy and more accurate determination of the camera motion trajectory.

Pose Estimation of a Noncooperative Target Based on Monocular Visual SLAM

This paper estimates the pose of a noncooperative space target utilizing a direct method of monocular visual simultaneous location and mapping (SLAM). A Large Scale Direct SLAM (LSD-SLAM) algorithm for pose estimation based on photometric residual of pixel intensities is provided to overcome the limitation of existing feature-based on-orbit pose estimation methods. Firstly, new sequence images of the on-orbit target are continuously inputted, and the pose of each current frame is calculated according to minimizing the photometric residual of pixel intensities. Secondly, frames are distinguished as keyframes or normal frames according to the pose relationship, and these frames are used to optimize the local map points. After that, the optimized local map points are added to the back-end map. Finally, the poses of keyframes are further enumerated and optimized in the back-end thread based on the map points and the photometric residual between the keyframes. Numerical simulations and experiments are carried out to prove the validity of the proposed algorithm, and the results elucidate the effectiveness of the algorithm in estimating the pose of the noncooperative target.

ASSESSMENT OF EARTHQUAKE-INDUCED DAMAGE LEVEL ON BUILDINGS: ANALYSIS OF TWO DIFFERENT SURVEY METHODS FOR A CASE STUDY

<p><strong>Abstract.</strong> Purpose of this paper is to compare different methods for the assessment of earthquake-induced damage on buildings based on survey data, referring to the case study of Castelluccio di Norcia. The seismic events that occurred in Central Italy in 2016 threatened the future of many villages located along the Apennine ridge straddling the Regions of Umbria, Marche, Abruzzo and Lazio: in Castelluccio di Norcia, a minor historical center in the Municipality of Norcia (Umbria), the earthquake occurred on August the 24^th caused some damage on localized buildings, but the strongest seismic event &amp;ndash; the one occurred on October the 30^th and with magnitude M_w 6.5, provoked numerous collapses and widespread failure on several buildings of the village, razing to the ground almost the 60&amp;thinsp;% of the built heritage. After the two earthquakes respectively, the Fire Brigade organized a reconnaissance campaign and flew over the village via UAVs (Unmanned Aerial Vehicles). By acquiring the videos recorded in this framework, that are currently available on the net and originally not recorded for specific survey purposes, the authors produced 3D models of the village allowing to compare the configuration of Castelluccio in the phases pre- and post- the destructive event of 30^th October: in fact, since the level of damage after the earthquake of August the 24^th was very low, the model resulting from the video recorded after this earthquake could be used as a model showing the layout of the village before the main struck of October. The result of this study is the Damage Degree Evaluation (DDE) and the following definition of a map showing for each building its class of damage, according to the distinction provided by European Macro-seismic Scale EMS98. On the other hand, another damage level map was studied: the Civil Protection requested, immediately after the earthquake and for the management of disaster response activities, the activation of the COPERNICUS project, providing for the detection of most damaged buildings of the village for an early census of the non-safe areas. The map of the damage level produced within this project is of course more accurate and precise, since it was collected through different acquisition systems: UAV, close-range photogrammetry, LiDAR (Light Detection And Ranging) and SLAM (Simultaneous Location and Mapping)-based mapping. This paper proposes a comparison between the two different DDEs, in order to define whether the first method, even if based on data downloaded via the web and therefore at lower resolution, and even if acquired with a more rapid evaluation procedure not providing for ground-based surveys, can lead to the construction of damage level maps that are plausible and realistic. The question is if the first method of DDE, even if less accurate, can allow to obtain results that are satisfactory and useful in the process of management and monitoring of natural hazards, providing support for the several implied institutions, in terms of information on catastrophes and first disaster rescue management.</p>