The Rolling Shutter (RS) mechanism is widely used in consumer-grade cameras, which are essential parts in smartphones and autonomous vehicles. RS leads to image distortion when the camera moves relative to the scene while capturing images. This effect needs to be considered in structure-from-motion, and vision-aided odometry, for which recent studies have extended Global Shutter (GS) methods to RS cameras. So far, it has been unclear how the RS affects spatiotemporal calibration of the camera in a multi-sensor system, which is crucial for good performance of aforementioned applications. This work takes the camera-IMU system as an example and looks into how the RS affects its spatiotemporal calibration. We develop a continuous-time calibration method for a RS camera-IMU system based on basis splines by using a calibration target. Considering the RS, every observation of a landmark on the target corresponds to a unique camera pose fitted by B-splines. Tests conducted on simulated data generated from public calibration datasets showed that the RS can noticeably affect accuracy of the camera extrinsic parameters, causing errors about 0.5° in orientation and 2 cm in translation with a setting common to smartphone cameras. With real data collected by two industrial camera-IMU systems, we found that considering the RS effect gives more accurate and consistent spatiotemporal calibration. These tests also revealed the impact of alternative IMU models and of the continuous-time versus discrete-time representation on camera extrinsic calibration, and the degradation of visual inertial odometry by extrinsic calibration errors.

The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers the camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed.

Unmanned Aerial System (UAS) technology is nowadays willingly used in small area topographic mapping due to low costs and good quality of derived products. Since cameras typically used with UAS have some limitations, e.g. cannot penetrate the vegetation, LiDAR sensors are increasingly getting attention in UAS mapping. Sensor developments reached the point when their costs and size suit the UAS platform, though, LiDAR UAS is still an emerging technology. One issue related to using LiDAR sensors on UAS is the limited performance of the navigation sensors used on UAS platforms. Therefore, various hardware and software solutions are investigated to increase the quality of UAS LiDAR point clouds. This work analyses several aspects of the UAS LiDAR point cloud generation performance based on UAS flights conducted with the Velodyne laser scanner and cameras. The attention was primarily paid to the trajectory reconstruction performance that is essential for accurate point cloud georeferencing. Since the navigation sensors, especially Inertial Measurement Units (IMUs), may not be of sufficient performance, the estimated camera poses could allow to increase the robustness of the estimated trajectory, and subsequently, the accuracy of the point cloud. The accuracy of the final UAS LiDAR point cloud was evaluated on the basis of the generated DSM, including comparison with point clouds obtained from dense image matching. The results showed the need for more investigation on MEMS IMU sensors used for UAS trajectory reconstruction. The accuracy of the UAS LiDAR point cloud, though lower than for point cloud obtained from images, may be still sufficient for certain mapping applications where the optical imagery is not useful.

Comparing typical airborne mapping systems with Unmanned Airborne Systems (UAS) developed for mapping purposes, there are several advantages and disadvantages of both systems. The unquestionable benefits of UAS are the much lower costs of equipment and the simple operation; though, the regulations to fly UAS greatly vary by country. Low cost, however, means small sensor size and low weight, thus, sensors usually lack the quality, negatively impacting the accuracy of UAS data and, consequently, any derived mapping products. This work compares the performance of three different positioning approaches used for UAS image geolocation. The first one is based on using dual-frequency GPS data, post-processed in kinematic mode. The second approach uses the single frequency, code only GPS data that was acquired and processed by a geotagger, attached to mapping camera. Finally, the third one employs indirect image georeferencing, based on aerial triangulation using ground controls. As expected, the quality of data provided by the inexpensive GPS receiver (geotagger) is not suitable for mapping purposes. The two other approaches provided similar and reliable results, confirming that commonly used indirect georeferencing, which usually assures good solution, can be replaced by direct georeferencing. The latter technique results not only in reduction of field work, e.g. Ground Control Points (GCPs) surveying, but is appropriate for use with other sensors, such as active imaging technology, LiDAR, further extending UAS application potential.