A novel 2.5D pattern for extrinsic calibration of ToF and camera fusion system

A novel 2.5D pattern for extrinsic calibration of ToF and camera fusion system,10.1109/IROS.2011.6048877,Jiyoung Jung,Yekeun Jeong,Hyowon Ha,James Dok

A novel 2.5D pattern for extrinsic calibration of ToF and camera fusion system  
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Recently, many researchers have made efforts for accurate calibration of a Time-of-Flight camera to fully utilize its provided depth values. Yet most previous works focus mainly on intrinsic calibration by modeling its systematic errors and noises while extrinsic calibration is also an important factor when constructing sensor fusion system. In this paper, we present a calibration process that can correctly transfer the depth measurements onto the color image. We use 2.5D pattern so that sufficient reprojection error can be considered for both color and ToF cameras. The issues on obtaining the correct correspondences for this pattern are discussed. In the optimization stage, the depth constraint is also employed to ensure the depth measurements to lie on the pattern plane. The strengths of the proposed method over previous approaches are evaluated in several robotic applications which require precise ToF and camera calibration. I. INTRODUCTION Accurate depth estimation of the scene has been one of the key research interests for past decades. This field is essential for a wide spectrum of robot applications, especially regarding navigation related tasks such as path-planning, obstacle avoidance, and 3D mapping. However, image based depth estimation often results in an inaccurate solution due to its inevitable ambiguity. Therefore, the need for metric depth measurement has led people to use such devices as 2D laser range finders and 3D Time-of-Flight cameras. A. Metric sensors for robots 2D laser range finders are commonly used to a large number of today's mobile robots due to their high speed and accuracy as well as large operational ranges. They are shown to be very effective for various tasks of mobile robots including map building, localization, and obstacle detection(6), (18), (16). However, since a 2D LRF scans a line at a time, it is usually equipped on a robot platform to scan its surroundings horizontally, and therefore the depth measurement is limited to the horizontal plane at the height of the sensor. Surmann et al.(18) propose a 3D LRF system by mounting a 2D LRF on a standard servo, which is controlled to rotate the mounted sensor vertically. Though
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