Stefan Fuchs and Gerd Hirzinger
Recently, ToF-cameras have attracted attention because of their ability to generate a full 2 1/2 D depth image at video frame rate. Thus, ToF-cameras are suitable for real-time 3D tasks such as tracking, visual servoing or object pose estimation. The usability of such systems mainly depends on an accurate camera calibration. In this work a calibration process for ToF-cameras with respect to the intrinsic parameters, the depth measurement distortion and the pose of the camera to a robot’s endeffector is described. The calibration process is not only based on the monochromatic images of the camera but also uses its depth values that are generated from a chequer-board pattern. The robustness and accuracy of the presented method is assessed applying it to randomly selected shots and comparing the calibrated measurements to a ground truth obtained from a laser scanner.
Stefan Fuchs and Stefan May
This paper presents a method for precise surface reconstruction with time-of-flight (TOF) cameras. A novel calibration approach which simplifies the calibration task and doubles the camera’s precision is developed and compared to current calibration methods. Remaining errors are tackled by applying filter and error distributing methods. Thus, a reference object is circumferentially reconstructed with an overall mean precision of approximately 3mm in translation and 3 deg in rotation. The resulting model serves as quantification of achievable
reconstruction precision with TOF cameras. This is a major criteria for the potential analysis of this sensor technology, that is firstly demonstrated within this work.
Cang Ye and GuruPrasad M. Hegde
This paper presents a new method for extracting object edges from range images obtained by a 3D range imaging sensor⎯the SwissRanger SR-3000. In range image preprocessing tage, the method enhances object edges by using surface normal information; and it employs the Hough Transform to detect straight line features in the Normal-Enhanced Range Image NERI). Due to the noise in the sensor’s range data, a NERI contains corrupted object surfaces that may result in unwanted edges and greatly encumber the extraction of linear features. To alleviate this problem, a Singular Value Decomposition (SVD) filter is developed to smooth object surfaces. The efficacy of the edge extraction method is validated by experiments in various environments.