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Time-of-flight cameras provide depth information, which is complementary to the photometric appearance of the scene in ordinary images. It is desirable to merge the depth and colour information, in order to obtain a coherent scene representation. However, the individual cameras will have different viewpoints, resolutions and fields of view, which means that they must be mutually calibrated. This paper presents a geometric framework for this multi-view and multi-modal calibration problem. It is shown that three-dimensional projective transformations can be used to align depth and parallax-based representations of the scene, with or without Euclidean reconstruction. A new evaluation procedure is also developed; this allows the reprojection error to be decomposed into calibration and sensor-dependent components. The complete approach is demonstrated on a network of three time-of-flight and six colour cameras. The applications of such a system, to a range of automatic scene-interpretation problems, are discussed.
Time-of-flight (TOF) cameras are sensors that can measure the depths of scene-points, by illuminating the scene with a controlled laser or LED source, and then analyzing the reflected light. In this paper, we will first describe the underlying measur
It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, bas
A laser calibration system was developed for monitoring and calibrating time of flight (TOF) scintillating detector arrays. The system includes setups for both small- and large-scale scintillator arrays. Following test-bench characterization, the las
Robot localization remains a challenging task in GPS denied environments. State estimation approaches based on local sensors, e.g. cameras or IMUs, are drifting-prone for long-range missions as error accumulates. In this study, we aim to address this
This paper proposes a new and efficient method to estimate 6-DoF ego-states: attitudes and positions in real time. The proposed method extract information of ego-states by observing a feature called TROVE (Three Rays and One VErtex). TROVE features a