Abstract
Abstract. Registration of RGB-D data using visual features is often influenced by errors in the transformation of visual features to 3D space as well as the random error of individual 3D points. In a long sequence, these errors accumulate and lead to inaccurate and deformed point clouds, particularly in situations where loop closing is not feasible. We present an epipolar search method for accurate transformation of the keypoints from 2D to 3D space, and define weights for the 3D points based on the theoretical random error of depth measurements. Our results show that the epipolar search method results in more accurate 3D correspondences. We also demonstrate that weighting the 3D points improves the accuracy of sensor pose estimates along the trajectory.
Highlights
Since their recent introduction to the market, RGB-D cameras, such as the Kinect (Microsoft, 2010), have gained a lot of popularity for indoor mapping, modelling and navigation
We look into two sources of error in pairwise registration based on visual features: the error in the transformation from the RGB space to the depth space, and the random error of individual points in the 3D space
We focus on two aspects in this approach: transformation of the colour image features to the depth image for the generation of 3D point correspondences, and weighting of the 3D point pairs based on the theoretical random error of individual points
Summary
Since their recent introduction to the market, RGB-D cameras, such as the Kinect (Microsoft, 2010), have gained a lot of popularity for indoor mapping, modelling and navigation. The common approach is based on visual features, i.e. point correspondences extracted from the colour images by keypoint extraction and matching methods such as SIFT (Lowe, 2004) and SURF (Bay et al, 2008) These point correspondences are transformed to 3D space by using the depth data, and are used to estimate the rotation and translation between every pair of frames. Loop closing is not always feasible, for example when mapping a long narrow corridor, or when the two frames at the closing do not have sufficient overlap or reliable keypoint matches In such situations, improvement of the pairwise registrations is important as it can reduce the error and deformations in the final point cloud.
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