The need for accurate 3D reconstructions of complex and large environments or structures has risen dramatically in recent years. In this context, devices known as portable mobile mapping systems have lately emerged as fast and accurate reconstruction solutions. While most of the research and commercial works have relied so far on laser scanners, solutions solely based on cameras and photogrammetry are attracting an increasing interest for the minor costs, size and power consumption of cameras. This thesis presents a novel handheld mobile mapping system based on stereo vision and image-based 3D reconstruction techniques. The main novelty of the system is that it leverages Visual Simultaneous Localization And Mapping (V-SLAM) technology to support and control the acquisition of the images. The real-time estimates of the system trajectory and 3D structure of the scene are used not only to enable a live feedback of the mapped area, but also to optimize the saving of the images, provide geometric and radiometric quality measures of the imagery, and robustly control the acquisition parameters of the cameras. To the best of authors’ knowledge, the proposed system is the first handheld mobile mapping system to offer these features during the acquisition of the images, and the results support its advantages in enabling accurate and controlled visual mapping experiences even in complex and challenging scenarios.

When reconstructing the Earth in 3D, the imagery can come from various mediums, including satellites, planes, and drones. One significant benefit of utilizing drones in combination with a Visual Simultaneous Localization and Mapping (V-SLAM) system is that specific areas of the world can be accurately mapped in real-time at a low cost. Drones can essentially be equipped with any camera sensor, but most commercially available drones use a monocular rolling shutter camera sensor. Therefore, on behalf of Maxar Technologies, multiple monocular V-SLAM systems were studied during this thesis, and ORB-SLAM3 and LDSO were determined to be evaluated further. In order to provide an accurate and reproducible result, the methods were benchmarked on the public datasets EuRoC MAV and TUM monoVO, which includes drone imagery and outdoor sequences, respectively. A third dataset was collected with a DJI Mavic 2 Enterprise Dual drone to evaluate how the methods would perform with a consumer-friendly drone. The datasets were used to evaluate the two V-SLAM systems regarding the generated 3D map (point cloud) and estimated camera trajectory. The results showed that ORB-SLAM3 is less impacted by the artifacts caused by a rolling shutter camera sensor than LDSO. However, ORB-SLAM3 generates a sparse point cloud where depth perception can be challenging since it abstracts the images using feature descriptors. In comparison, LDSO produces a semi-dense 3D map where each point includes the pixel intensity, which improves the depth perception. Furthermore, LDSO is more suitable for dark environments and low-texture surfaces. Depending on the use case, either method can be used as long as the required prerequisites are provided. In conclusion, monocular V-SLAM systems are highly dependent on the type of sensor being used. The differences in the accuracy and robustness of the systems using a global shutter and a rolling shutter are significant, as the geometric artifacts caused by a rolling shutter are devastating for a pure visual pipeline. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

The Master thesis is focused on analyzing stereo camera ZED in the outdoor environment. There is compared ZEDfu visual odometry with commonly used methods like GPS or wheel odometry. Moreover, the thesis includes analyses of SLAM in the changeable outdoor environment, too. The simultaneous mapping and localization in RTAB-Map were processed separately with SIFT and BRISK descriptors. The aim of this master thesis is to analyze the behaviour ZED camera in the outdoor environment for future implementation in mobile robotics.