/tags/calibration/ Recent content in Calibration on ViCoS Lab Hugo en-us /publications/muhovic2023joint/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/muhovic2023joint/ <p>Multimodal sensor systems require precise calibration if they are to be used in the field. Due to the difficulty of obtaining the corresponding features from different modalities, the calibration of such systems is an open problem. We present a systematic approach for calibrating a set of cameras with different modalities (RGB, thermal, polarization, and dual-spectrum near infrared) with regard to a LiDAR sensor using a planar calibration target. Firstly, a method for calibrating a single camera with regard to the LiDAR sensor is proposed. The method is usable with any modality, as long as the calibration pattern is detected. A methodology for establishing a parallax-aware pixel mapping between different camera modalities is then presented. Such a mapping can then be used to transfer annotations, features, and results between highly differing camera modalities to facilitate feature extraction and deep detection and segmentation methods.</p> /publications/muhovic2022towards/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/muhovic2022towards/ <p>The robustness of autonomous vehicles can be significantly improved by using multiple sensor modalities. In addition to standard color cameras and less frequently used thermal, multispectral and polarization cameras, LIDAR and RADAR are most often used sensors, and are largely complementary to image sensors. However, the spatial calibration of such a system can be extremely challenging due to the difficulties in obtaining corresponding features from different modalities, as well as the inevitable parallax arising from different sensor positions. In this paper, we present a comprehensive strategy for calibrating such a system using a multi-modal target, and illustrate how such a strategy could be upgraded to an fully automatic, target-less calibration that would rely on features of the scene itself to align at least small sensor offsets from the calibrated position. We find that a high-level understanding of the scene is ideal for this task, as this way we can identify characteristic points for spatial alignment of sensor data of different modalities.</p>