RESUMEN
We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.
RESUMEN
Attitude jitter is a crucial factor that limits the imaging quality and geo-positioning accuracy of high-resolution optical satellites, which has attracted significant research interests in recent years. However, few researchers have attempted to retrieve the dynamic characteristics and time-varying trends of a satellite attitude jitter. This paper presents a novel processing framework for detecting, estimating, and investigating time-varying attitude jitter in long strips based on a time-frequency analysis with the input from either an attitude sensor or an optical imaging sensor. Attitude angle signals containing attitude jitter information are detected from attitude data through generating the Euler angles relative to the orbit coordinate system, or from image data through high-accuracy dense matching between parallax observations, correction of integration time variation and frequency domain-based deconvolution. Variational mode decomposition is adopted to extract the separate band-limited periodic components, and Hilbert spectral analysis is integrated to estimate the instantaneous attributes for each time sample and the varying trends for the entire duration. Experiments with three sets of ZiYuan-3 long-strip datasets were carried out to test the novel processing framework of attitude jitter. The experimental results indicate that the processing framework could reveal the dynamic jitter characteristics, and the mutual validations of different data sources demonstrate the effectiveness of the proposed method.
RESUMEN
Automatic alignment estimation from projection images has a range of applications, but misaligned cameras induce inaccuracies. Calibration methods for optical cameras requiring calibration bodies or detectable features have been a matter of research for years. Not so for image guided therapy, although exact patient pose recovery is crucial. To image patient anatomy, X-ray instead of optical equipment is used. Feature detection is often infeasible. Furthermore, a method not requiring a calibration body, usable during treatment, would be desirable to improve accuracy of the patient alignment. We present a novel approach not relying on image features but combining intensity based calibration with 3D pose recovery. A stereoscopic X-ray camera model is proposed, and effects of erroneous parameters on the patient alignment are evaluated. The relevant camera parameters are automatically computed by comparison of X-ray to CT images and are incorporated in the patient alignment computation. The methods were tested with ground truth data of an anatomic phantom with artificially produced misalignments and available real-patient images from a particle therapy machine. We show that our approach can compensate patient alignment errors through mis-calibration of a camera from more than 5 mm to below 0.2 mm. Usage of images with artificial noise shows that the method is robust against image degradation of 2-5%. X-ray camera self-calibration improves accuracy when cameras are misaligned. We could show that rigid body alignment was computed more accurately and that self-calibration is possible, even if detection of corresponding image features is not.