OBLIQUE MULTI-CAMERA SYSTEMS - ORIENTATION AND DENSE MATCHING ISSUES

International audience3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy <rupnik, franex, remondino>@fbk.eu, http://3dom.fbk.eu Commission III-WG4 ABS TRACT: The use of oblique imagery has become a standard for many civil and mapping applications, thanks to the development of airborne digital multi-camera systems, as proposed by many companies (Blomoblique, IGI, Leica, M idas, Pictometry, Vexcel/M icrosoft, VisionM ap, etc.). The indisputable virtue of oblique photography lies in its simplicity of interpretation and understanding for inexperienced users allowing their use of oblique images in very different applications, such as building detection and reconstruction, building structural damage classification, road land updating and administration services, etc. The paper reports an overview of the actual oblique commercial systems and presents a workflow for the automated orientation and dense matching of large image blocks. Perspectives, potentialities, pitfalls and suggestions for achieving satisfactory results are given. Tests performed on two datasets acquired with two multi-camera systems over urban areas are also reported. Figure 1: Large urban area pictured with an oblique multi-camera system. Once advanced image triangulation methods have retrieved interior and exterior parameters of the cameras, dense point clouds can be deriv ed for 3D city modelling, feature extraction and mapping purposes

Integration of range and image data for building reconstruction

The extraction of information from image and range data is one of the main research topics. In literature, several papers dealing with this topic has been already presented. In particular, several authors have suggested an integrated use of both range and image information in order to increase the reliability and the completeness of the results exploiting their complementary nature. In this paper, an integration between range and image data for the geometric reconstruction of man-made object is presented. The focus is on the edge extraction procedure performed in an integrated way exploiting both the from range and image data. Both terrestrial and aerial applications have been analysed for the faade extraction in terrestrial acquisitions and the roof outline extraction from aerial data. The algorithm and the achieved results will be described and discussed in detail

Microdrone-Based Indoor Mapping with Graph SLAM

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm

VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VEHICLES USING SEGMENTATION-INITIALISED TRACKERS

We propose an effective vehicle tracker and speed estimation method from Unmanned Aerial Vehicles (UAVs) videos that can be deployed on UAV-embedded edge devices. Our tracker uses segmentation-derived vehicle regions to initialise a MOSSE tracker. This enables road operators to make multipurpose use of segmentation outputs while still being able to track the vehicles across frames. The vehicle speed is estimated using flight parameters derived from the UAV's flight computer and the vehicle displacement across frames. We trained CABiNet on the UAVid urban segmentation benchmark dataset and finetuned it on a dataset collected at our study site. A mean Intersection over Union (mIoU) of 0.73 was obtained for the vehicle class. Our segmentation-initialised MOSSE tracker was evaluated on the VisDrone Multi-Object Tracking (MOT) benchmark dataset and compared against traditional methods that utilise object regions for tracker initialisation. Our approach yielded a Multi-Object Tracking Precision (MOTP) of 0.872 compared to 0.830 when using YOLOv4. Our vehicle speed estimations approach was evaluated using a privately collected ground truth vehicle speed dataset. Our approach yielded a Root Mean Square Error (RMSE) between 3.42 and 16.12 km/hr across different flight configurations. Finally, our approach was deployed on an NVIDIA Jetson Xavier NX edge device and could be executed at 8 Frames Per Second (FPS). The results indicate that our approach is a simple yet fast alternative to traditional tracking methods while producing multipurpose segmentation information

BENCHMARKING THE EXTRACTION OF 3D GEOMETRY FROM UAV IMAGES WITH DEEP LEARNING METHODS

3D reconstruction from single and multi-view stereo images is still an open research topic, despite the high number of solutions proposed in the last decades. The surge of deep learning methods has then stimulated the development of new methods using monocular (MDE, Monocular Depth Estimation), stereoscopic and Multi-View Stereo (MVS) 3D reconstruction, showing promising results, often comparable to or even better than traditional methods. The more recent development of NeRF (Neural Radial Fields) has further triggered the interest for this kind of solution. Most of the proposed approaches, however, focus on terrestrial applications (e.g., autonomous driving or small artefacts 3D reconstructions), while airborne and UAV acquisitions are often overlooked. The recent introduction of new datasets, such as UseGeo has, therefore, given the opportunity to assess how state-of-the-art MDE, MVS and NeRF 3D reconstruction algorithms perform using airborne UAV images, allowing their comparison with LiDAR ground truth. This paper aims to present the results achieved by two MDE, two MVS and two NeRF approaches levering deep learning approaches, trained and tested using the UseGeo dataset. This work allows the comparison with a ground truth showing the current state of the art of these solutions and providing useful indications for their future development and improvement