Terrestrial Mobile Mapping Research Articles

Design of structured meshes of mining excavations based on variability trends of real point clouds from laser scanning for numerical airflow modeling

Various technologies are used to acquire and process 3D data from mining excavations, such as Terrestrial Laser Scanning (TLS), photogrammetry, or Mobile Mapping Systems (MMS) supported by Simultaneous Localization and Mapping (SLAM) algorithms. Due to the often difficult measurement conditions, the data obtained are often incomplete or inaccurate. There are gaps in the point cloud due to objects obscuring the tunnel. Data processing itself is also time-consuming. Point clouds must be cleaned of unnecessary noise and elements. On the other hand, accurate modeling of airflows is an ongoing challenge for the scientific community. Considering the utilization of 3D data for the numerical analysis of airflow in mining excavations using Computational Fluid Dynamics (CFD) tools, this poses a considerable problem, especially the creation of a surface mesh model, which could be further utilized for this application. This paper proposes a method to create a synthetic model based on real data. 3D data from underground mining tunnels captured by a LiDAR sensor are processed employing feature extraction. A uniformly sampled tunnel of given dimensions, point cloud resolution, and cross-sectional shape is created for which obtained features are applied, e.g. general trajectory of the tunnel, shapes of walls, and additional valuable noise for obtaining surfaces of desired roughness. This allows to adjust parameters such as resolution, dimensions, or strengths of features to obtain the best possible representation of a real underground mining excavation geometry. From a perspective of Computational Fluid Dynamics (CFD) simulations of airflow, this approach has the potential to shorten geometry preparation, increase the quality of computational meshes, reduce discretization time, and increase the accuracy of the results obtained, which is of particular importance considering airflow modeling of extensive underground ventilation networks.

Assessment of Leica CityMapper-2 LiDAR Data within Milan’s Digital Twin Project

The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM.

Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system

Forest field inventory plays a crucial role in forestry management and the estimation of carbon circular economy, as it provides information on forest parameters, assesses carbon storage, and identifies the impact factors of ecological change. Terrestrial Laser Scanning (TLS) and Mobile Mapping Systems (MMS) are commonly used for forest field inventory, but they are unable to verify the inventory results in real time. With real-time forest inventory, forestry workers can immediately verify if all trees have been correctly extracted without requiring expertise in point clouds. The advences of miniaturized, low-cost 3D sensors (such as solid-state laser scanners and Inertial Measurement Units [IMUs]) and edge computing units have made it possible to achieve real-time forest inventory using a compact, low-cost helmet. To this end, this paper presents a real-time automated forest field inventory method, which is validated on a compact, low-cost helmet-based laser scanning system. Firstly, a Fast Candidate Tree Detection (FCTD) approach is proposed to identify individual trees as candidates by utilizing a novel 2D corner detection technique based on point cloud projection, taking into account point density and geometry features. Secondly, a Spatiotemporal Consistency-based Tree Parameter Estimation (SCTPE) method is proposed to estimate the tree parameters by considering both the current submap and submaps that have been scanned in real-time. The proposed method was tested in three typical forest areas in Wuhan, China, where the main tree species present include Sapindaceae, dawn redwoods, and Platycladus. The results showed that the proposed method achieves high accuracy in tree detection (recall=0.97,precision=0.94,F=0.96). The average error and root-mean-square error (RMSE) of DBH are 0.033 m and 0.038 m, which outperforms the existing one-circle fitting model. The average error and RMSE of tree height are 0.231 m and 0.294 m. Overall, these results demonstrate the high potential of the helmet-based laser scanning system for real-time forest field inventory.

Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks

Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top ofa ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to concludethat the developed TMMS is suitable to capture data regarding urban vegetation.

쿼터니언과 프로크루스테스를 이용한 점대선기반 3차원 좌표변환

3D coordinate transformation is the process of matching different three-dimensional coordinate systems equally, and is an important task in photogrammetry, computer vision, and robotics. This study proposed a method of using quaternions and Procrustes algorithms to fit different three-dimensional coordinate systems using points and straight lines. Rotational elements were calculated using quaternions by projecting the coordinates of the points in the model coordinate system onto those in the reference coordinate system. For the scale factor and three-dimensional translation, Procrustes algorithms using points and straight lines were then used. Indoor experiments using checker-boards were conducted, followed by experiments using outdoor data acquired from a terrestrial mobile mapping system. It was found that more than four point-to-line pairs are needed to perform three-dimensional transformation reliably.

INTEGRATION OF AERIAL, MMS, AND BACKPACK IMAGES FOR SEAMLESS 3D MAPPING IN URBAN AREAS

Abstract. Photorealistic three-dimensional (3D) models play an indispensable role in the spatial data infrastructure (SDI) of a smart city. Recent developments in aerial oblique photogrammetry, and the popularity of terrestrial mobile mapping systems (MMSs) offer possibilities for deriving 3D models with centimeter-level accuracy in urban areas. Additionally, advances in image matching and bundle adjustment have allowed 3D models derived from the integration of aerial and ground imagery to overcome typical problems related to 3D mapping in urban areas (e.g., geometric defects, blurred textures on building façades). Nevertheless, this approach may not be suitable for all scenarios owing to innate differences between each platform. Besides, MMS images may not cover regions that cannot be reached by mobile vehicles in urban areas (e.g., narrow alleys, areas far from roads). Meanwhile, backpack systems have garnered attention from the photogrammetry community in recent years due to their flexibility, and regions neglected in previous works can be adequately reconstructed from images collected by backpack systems. This paper presents an approach for effectively integrating multi-source images collected by aerial, MMS, and backpack platforms for seamless 3D mapping in urban areas. The approach includes three main steps: (1) data pre-processing, (2) combined structure-from-motion, and (3) optimal generation of a textured 3D mesh model. The experimental results using aerial, MMS, and backpack datasets collected in a typical urban area in Hong Kong demonstrate the promising performance of the proposed approach. The described work is significant for boosting various types of imagery for integrated 3D mapping in both city scale and street level to facilitate various applications.

A Fisheye Image Matching Method Boosted by Recursive Search Space for Close Range Photogrammetry

Close range photogrammetry (CRP) with large field-of-view images has become widespread in recent years, especially in terrestrial mobile mapping systems (TMMS). However, feature-based matching (FBM) with omnidirectional images (e.g., fisheye) is challenging even for state-of-the-art methods, such as the scale-invariant feature transform (SIFT), because of the strong scale change from image to image. This paper proposes an approach to boost FBM techniques on fisheye images with recursive reduction of the search space based on epipolar geometry. The epipolar restriction is calculated with the equidistant mathematical model and the initial exterior orientation parameters (EOPs) determined with navigation sensors from TMMS. The proposed method was assessed with data sets acquired by a low-cost TMMS. The TMMS is composed of a calibrated poly-dioptric system (Ricoh Theta S) and navigation sensors aimed at outdoor applications. The assessments show that Ricoh Theta S position and attitude were estimated in a global bundle adjustment with a precision (standard deviation) of 4 cm and 0.3°, respectively, using as observations the detected matches from the proposed method. Compared with other methods based on SIFT extended to the omnidirectional geometry, our approach achieved compatible results for outdoor applications.

Three-dimensional data collection for coastal management – efficiency and applicability of terrestrial and airborne methods

ABSTRACTRegular monitoring is essential to understand coastal morphodynamics and anthropic as well as natural impacts, at different temporal and spatial scales. A stereoscopic video-based terrestrial mobile mapping system, three airborne digital photography systems (mounted on a small manned airplane, a fixed-wing UAV and a multi-rotor UAV, respectively) and airborne LiDAR were compared in terms of: system features, such as range, autonomy, acquisition and operating costs; information supplied, its type and precision; and constraints to system applicability in coastal topographic surveys. Systems differed in resolution, efficiency, and applicability. The terrestrial and UAV-based systems provided the most accurate 3D data, being particularly suited for small-scale, high-resolution surveys. UAVs were easy to deploy, but limited by weather condition, particularly wind speed. Observations from a plane were most efficient and suited for larger areas. Airborne systems had the advantage of being less (UAV) to non-invasive (plane) and thus suitable for the monitoring of sensitive areas (e.g. dunes) and/or areas with difficult access. Systems should be chosen according to the specific survey aims, spatial scale, and local conditions, taking into account their applicability and cost-benefit ratios. They may complement each other to provide a comprehensive picture of coastal morphology and dynamics at different scales.

APPLICATION OF MLS DATA TO THE ASSESSMENT OF SAFETY-RELATED FEATURES IN THE SURROUNDING AREA OF AUTOMATICALLY DETECTED PEDESTRIAN CROSSINGS

Abstract. During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

LARGE SCALE TEXTURED MESH RECONSTRUCTION FROM MOBILE MAPPING IMAGES AND LIDAR SCANS

Abstract. The representation of 3D geometric and photometric information of the real world is one of the most challenging and extensively studied research topics in the photogrammetry and robotics communities. In this paper, we present a fully automatic framework for 3D high quality large scale urban texture mapping using oriented images and LiDAR scans acquired by a terrestrial Mobile Mapping System (MMS). First, the acquired points and images are sliced into temporal chunks ensuring a reasonable size and time consistency between geometry (points) and photometry (images). Then, a simple, fast and scalable 3D surface reconstruction relying on the sensor space topology is performed on each chunk after an isotropic sampling of the point cloud obtained from the raw LiDAR scans. Finally, the algorithm proposed in (Waechter et al., 2014) is adapted to texture the reconstructed surface with the images acquired simultaneously, ensuring a high quality texture with no seams and global color adjustment. We evaluate our full pipeline on a dataset of 17 km of acquisition in Rouen, France resulting in nearly 2 billion points and 40000 full HD images. We are able to reconstruct and texture the whole acquisition in less than 30 computing hours, the entire process being highly parallel as each chunk can be processed independently in a separate thread or computer.

Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.

OPEN SOURCE WEB TOOL FOR TRACKING IN A LOWCOST MOBILE MAPPING SYSTEM

Abstract. During the last decade several Mobile Mapping Systems (MMSs), i.e. systems able to acquire efficiently three dimensional data using moving sensors (Guarnieri et al., 2008, Schwarz and El-Sheimy, 2004), have been developed. Research and commercial products have been implemented on terrestrial, aerial and marine platforms, and even on human-carried equipment, e.g. backpack (Lo et al., 2015, Nex and Remondino, 2014, Ellum and El-Sheimy, 2002, Leica Pegasus backpack, 2016, Masiero et al., 2017, Fissore et al., 2018). Such systems are composed of an integrated array of time-synchronised navigation sensors and imaging sensors mounted on a mobile platform (Puente et al., 2013, Tao and Li, 2007). Usually the MMS implies integration of different types of sensors, such as GNSS, IMU, video camera and/or laser scanners that allow accurate and quick mapping (Li, 1997, Petrie, 2010, Tao, 2000). The typical requirement of high-accuracy 3D georeferenced reconstruction often makes such systems quite expensive. Indeed, at time of writing most of the terrestrial MMSs on the market have a cost usually greater than 50000, which might be expensive for certain applications (Ellum and El-Sheimy, 2002, Piras et al., 2008). In order to allow best performance sensors have to be properly calibrated (Dong et al., 2007, Ellum and El-Sheimy, 2002). Sensors in MMSs are usually integrated and managed through a dedicated software, which is developed ad hoc for the devices mounted on the mobile platform and hence tailored for the specific used sensors. Despite the fact that commercial solutions are complete, very specific and particularly related to the typology of survey, their price is a factor that restricts the number of users and the possible interested sectors. This paper describes a (relatively low cost) terrestrial Mobile Mapping System developed at the University of Padua (TESAF, Department of Land Environment Agriculture and Forestry) by the research team in CIRGEO, in order to test an alternative solution to other more expensive MMSs. The first objective of this paper is to report on the development of a prototype of MMS for the collection of geospatial data based on the assembly of low cost sensors managed through a web interface developed using open source libraries. The main goal is to provide a system accessible by any type of user, and flexible to any type of upgrade or introduction of new models of sensors or versions thereof. After a presentation of the hardware components used in our system, a more detailed description of the software developed for the management of the MMS will be provided, which is the part of the innovation of the project. According to the worldwide request for having big data available through the web from everywhere in the world (Pirotti et al., 2011), the proposed solution allows to retrieve data from a web interface Figure 4. Actually, this is part of a project for the development of a new web infrastructure in the University of Padua (but it will be available for external users as well), in order to ease collaboration between researchers from different areas. Finally, strengths, weaknesses and future developments of the low cost MMS are discussed.

Mobile mapping systems and spatial data collection strategies assessment in the identification of horizontal alignment of highways

The horizontal alignment of existing highways may be identified by using several terrestrial or aerial geomatics technologies. Such technologies involve different levels of precision and accuracy; hence, different results can be expected. At present, there are no comparisons available between the solutions resulting from the use of different technologies and data sources for the same road alignment.In this investigation, a number of terrestrial mobile mapping techniques and data collection strategies were evaluated. The centerline of a 3.6km section of a highway was used to estimate radii, centers of curvature and orientation of tangents. Two statistical fitting methods were used to back-calculate these parameters, and the results were then compared with as-built alignment data.Terrestrial images from a mobile mapping vehicle were used to determine the centerline, which was also estimated as the average line of the carriageway and pavement edges, and as the average line of the two driving trajectories. Positions were surveyed using low-cost sensors (an integrated GPS-IMU platform, HD webcam). For comparison purposes, aerial orthophotos and a GNSS (high-cost) receiver were used simultaneously. Although the GPS-IMU data and estimated trajectories provided results comparable to those of the GNSS receiver, the use of georeferenced images proved less accurate. The results and comments in the paper should be of use to survey practitioners when they need to select an acquisition methodology appropriate to the desired level of accuracy and in line with budget constraints.

First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping

In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of μ E , N ≈ 1.7 px (3.4 cm) and μ h ≈ 4.3 px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective.

Accuracy analysis of a mobile mapping system for close range photogrammetric projects

Image-based mapping solutions require accurate exterior orientation parameters independently of the cameras used for a survey. This paper analyses the inclusion of up to two stereo-based geometric constraints in the form of baseline distance and convergence angle between camera axes to boost the integrated sensor orientation performance on outdoor close-range projects. A terrestrial low-cost mobile mapping GNSS/IMU multi-camera system is used to test the performance of the stereo-based geometric constraint on a weak geometric network in a stop-and-go survey. The influence of the number of control points (CPs) is analysed to confirm the performance and usability of the geometric constraints in real live terrestrial projects where far from ideal setups can exist across the survey. Improvements in image residuals up to 9 times and deviation errors better than 1 cm are expected when at least three CPs are incorporated into the adjustment.

HYBRID MODELS FOR TRAJECTORY ERROR MODELLING IN URBAN ENVIRONMENTS

Abstract. This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.

HYBRID MODELS FOR TRAJECTORY ERROR MODELLING IN URBAN ENVIRONMENTS

This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. <br><br> First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. <br><br> The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.

MapKITE: A NEW PARADIGM FOR SIMULTANEOUS AERIAL AND TERRESTRIAL GEODATA ACQUISITION AND MAPPING

We introduce a new mobile, simultaneous terrestrial and aerial, geodata collection and post-processing method: mapKITE. By combining two mapping technologies such as terrestrial mobile mapping and unmanned aircraft aerial mapping, geodata are simultaneously acquired from air and ground. More in detail, a mapKITE geodata acquisition system consists on an unmanned aircraft and a terrestrial vehicle, which hosts the ground control station. By means of a real-time navigation system on the terrestrial vehicle, real-time waypoints are sent to the aircraft from the ground. By doing so, the aircraft is linked to the terrestrial vehicle through a “virtual tether,” acting as a “mapping kite.” <br><br> In the article, we entail the concept of mapKITE as well as the various technologies and techniques involved, from aircraft guidance and navigation based on IMU and GNSS, optical cameras for mapping and tracking, sensor orientation and calibration, etc. Moreover, we report of a new measurement introduced in mapKITE, that is, point-and-scale photogrammetric measurements [of image coordinates and scale] for optical targets of known size installed on the ground vehicle roof. By means of accurate posteriori trajectory determination of the terrestrial vehicle, mapKITE benefits then from kinematic ground control points which are photogrametrically observed by point-and-scale measures. <br><br> Initial results for simulated configurations show that these measurements added to the usual Integrated Sensor Orientation ones reduce or even eliminate the need of conventional ground control points –therefore, lowering mission costs– and enable selfcalibration of the unmanned aircraft interior orientation parameters in corridor configurations, in contrast to the situation of traditional corridor configurations. <br><br> Finally, we report about current developments of the first mapKITE prototype, developed under the European Union Research and Innovation programme Horizon 2020. The first mapKITE mission will be held at the BCN Drone Center (Collsuspina, Moià, Spain) in mid 2016.

MapKITE: A NEW PARADIGM FOR SIMULTANEOUS AERIAL AND TERRESTRIAL GEODATA ACQUISITION AND MAPPING

Abstract. We introduce a new mobile, simultaneous terrestrial and aerial, geodata collection and post-processing method: mapKITE. By combining two mapping technologies such as terrestrial mobile mapping and unmanned aircraft aerial mapping, geodata are simultaneously acquired from air and ground. More in detail, a mapKITE geodata acquisition system consists on an unmanned aircraft and a terrestrial vehicle, which hosts the ground control station. By means of a real-time navigation system on the terrestrial vehicle, real-time waypoints are sent to the aircraft from the ground. By doing so, the aircraft is linked to the terrestrial vehicle through a “virtual tether,” acting as a “mapping kite.” In the article, we entail the concept of mapKITE as well as the various technologies and techniques involved, from aircraft guidance and navigation based on IMU and GNSS, optical cameras for mapping and tracking, sensor orientation and calibration, etc. Moreover, we report of a new measurement introduced in mapKITE, that is, point-and-scale photogrammetric measurements [of image coordinates and scale] for optical targets of known size installed on the ground vehicle roof. By means of accurate posteriori trajectory determination of the terrestrial vehicle, mapKITE benefits then from kinematic ground control points which are photogrametrically observed by point-and-scale measures. Initial results for simulated configurations show that these measurements added to the usual Integrated Sensor Orientation ones reduce or even eliminate the need of conventional ground control points –therefore, lowering mission costs– and enable selfcalibration of the unmanned aircraft interior orientation parameters in corridor configurations, in contrast to the situation of traditional corridor configurations. Finally, we report about current developments of the first mapKITE prototype, developed under the European Union Research and Innovation programme Horizon 2020. The first mapKITE mission will be held at the BCN Drone Center (Collsuspina, Moià, Spain) in mid 2016.

ODOMETRY AND LOW-COST SENSOR FUSION IN TMM DATASET

Abstract. The aim of this study is to identify the most powerful motion model and filtering technique to represent an urban terrestrial mobile mapping (TMM) survey and ultimately to obtain the best representation of the car trajectory. The authors want to test how far a motion model and a more or less refined filtering technique could bring benefits in the determination of the car trajectory. To achieve the necessary data for the application of the motion models and the filtering techniques described in the article, the authors realized a TMM survey in the urban centre of Turin by equipping a vehicle with various instruments: a low-cost action-cam also able to record the GPS trace of the vehicle even in the presence of obstructions, an inertial measurement system and an odometer. The results of analysis show in the article indicate that the Unscented Kalman Filter (UKF) technique provides good results in the determination of the vehicle trajectory, especially if the motion model considers more states (such as the positions, the tangential velocity, the angular velocity, the heading, the acceleration). The authors also compared the results obtained with a motion model characterized by four, five and six states. A natural corollary to this work would be the introduction to the UKF of the photogrammetric information obtained by the same camera placed on board the vehicle. These data would permit to establish how photogrammetric measurements can improve the quality of TMM solutions, especially in the absence of GPS signals (like urban canyons).