In this paper we present a novel approach to global localization using an RGB-D camera in maps of visual features. For large maps, the performance of pure image matching techniques decays in terms of robustness and computational cost. Particularly, repeated occurrences of similar features due to repeating structure in the world (e.g., doorways, chairs, etc.) or missing associations between observations pose critical challenges to visual localization. We address these challenges using a two-step approach. We first estimate a candidate pose using few correspondences between features of the current camera frame and the feature map. The initial set of correspondences is established by proximity in feature space. The initial pose estimate is used in the second step to guide spatial matching of features in 3D, i.e., searching for associations where the image features are expected to be found in the map. A RANSAC algorithm is used to compute a fine estimation of the pose from the correspondences. Our approach clearly outperforms localization based on feature matching exclusively in feature space, both in terms of estimation accuracy and robustness to failure and allows for global localization in real time (30Hz).
To the best of our knowledge, at the time being, there is no other dedicated global localization approach for the recently introduced RGB-D sensors. However, a number of novel approaches for visual odometry have been proposed, which exploit the available combination of color, density of depth and the high frame rate to improve alignment performance as compared, e.g., to the iterative closest point algorithm @cite_4 . In @cite_18 and @cite_2 adaptations of ICP are proposed to process the high amounts of data more efficiently. Steinbruecker @cite_28 present a transformation estimation based on the minimization of an energy function. For frames close to each other, they achieve enhanced runtime performance and accuracy compared to Generalized ICP @cite_22 . Using the distribution of normals, Osteen @cite_27 improve the initialization of ICP by efficiently computing the difference in orientation between two frames, which allows a substantial drift reduction. These approaches work well for computing the transformation for small incremental changes between consecutive frames, but they are of limited applicability for global localization in a map.
@cite_18 @cite_4 @cite_22 @cite_28 @cite_27 @cite_2
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This letter presents a novel method for visual odometry estimation from a RGB-D camera. The camera motion is estimated by aligning a source to a target RGB-D frame using an intensity-assisted iterative closest point (ICP) algorithm. The proposed method differs from the conventional ICP in following aspects. 1) To reduce the computational cost, salient point selection is performed on the source frame, where only points that contain valuable information for registration are used. 2) To reduce the influence of outliers and noises, robust weighting function is proposed to weight corresponding pairs based on statistics of their spatial distances and intensity differences. 3) The obtained robust weighting function from 2) is used for correspondence estimation of the following ICP iteration. The proposed method runs in real-time with a single core CPU thread, hence it is suitable for robots with limited computation resources. The evaluation on TUM RGB-D benchmark shows that in the majority of the tested sequences, our proposed method outperforms state-of-the-art accuracy in terms of translational drift per second with a computation speed of 78 Hz. This paper proposes an edge-based robust RGB-D visual odometry (VO)using 2-D edge divergence minimization. Our approach focuses on enabling the VO to operate in more general environments subject to low texture and changing brightness, by employing image edge regions and their image gradient vectors within the iterative closest points (ICP)framework. For more robust and stable ICP-based optimization, we propose a robust edge matching criterion with image gradient vectors. In addition, to reduce a bad effect of outlier residuals, we propose an improved edge registration problem of 2-D edge divergence minimization in the manner of an iterative reweight least squares (IRLS)motion estimation. To accelerate the proposed approach, a pixel sub-sampling method is employed. We evaluate estimation performance of our method in changing brightness conditions and low-textured scenes. Our approach shows more robust motion estimation than state-of-the-art methods while maintaining comparable accuracy in challenging image sequences at real-time (25 Hz)operation. We present a technique to estimate the egomotion of an RGB-D sensor based on rotations of functions defined on the unit sphere. In contrast to traditional approaches, our technique is not based on image features and does not require correspondences to be generated between frames of data. Instead, consecutive functions are correlated using spherical harmonic analysis. An Extended Gaussian Image (EGI), created from the local normal estimates of a point cloud, defines each function. Correlations are efficiently computed using Fourier transformations, resulting in a 3 Degree of Freedom (3-DoF) rotation estimate. An Iterative Closest Point (ICP) process then refines the initial rotation estimate and adds a translational component, yielding a full 6-DoF egomotion estimate. The focus of this work is to investigate the merits of using spherical harmonic analysis for egomotion estimation by comparison with alternative 6-DoF methods. We compare the performance of the proposed technique with that of stand-alone ICP and image feature based methods. As with other egomotion techniques, estimation errors accumulate and degrade results, necessitating correction mechanisms for robust localization. For this report, however, we use the raw estimates; no filtering or smoothing processes are applied. In-house and external benchmark data sets are analyzed for both runtime and accuracy. Results show that the algorithm is competitive in terms of both accuracy and runtime, and future work will aim to combine the various techniques into a more robust egomotion estimation framework. This paper presents a robust and efficient semidense visual odometry solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline which estimates the pose of the sensor by registering the projection of a 3D semidense map of a reference frame with the 2D semi-dense region extracted in the current frame. The processing is speeded up by efficiently implemented approximate nearest neighbour fields under the Euclidean distance criterion, which permits the use of compact Gauss-Newton updates in the optimization. The registration is formulated as a maximum a posterior problem to deal with outliers and sensor noise, and the equivalent weighted least squares problem is consequently solved by iteratively reweighted least squares method. A variety of robust weight functions are tested and the optimum is determined based on the probabilistic characteristics of the sensor model. Extensive evaluation on publicly available RGB-D datasets shows that the proposed method predominantly outperforms existing state-of-the-art methods.