Finding the camera pose is an important step in many egocentric video applications. It has been widely reported that, state of the art SLAM algorithms fail on egocentric videos. In this paper, we propose a robust method for camera pose estimation, designed specifically for egocentric videos. In an egocentric video, the camera views the same scene point multiple times as the wearer's head sweeps back and forth. We use this specific motion profile to perform short loop closures aligned with wearer's footsteps. For egocentric videos, depth estimation is usually noisy. In an important departure, we use 2D computations for rotation averaging which do not rely upon depth estimates. The two modification results in much more stable algorithm as is evident from our experiments on various egocentric video datasets for different egocentric applications. The proposed algorithm resolves a long standing problem in egocentric vision and unlocks new usage scenarios for future applications.
Dense methods use the entire image and not just few selected features @cite_15 . The camera poses are estimated as the set of parameters which minimize the image difference over all pixels in the image. To increase the accuracy of estimation, semi-dense methods are usually adopted, which perform photometric error minimization only in regions of sufficient gradient @cite_14 @cite_24 .
@cite_24 @cite_15 @cite_14
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In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several state-of-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source. The paper addresses the problem of camera tracking, which denotes the continuous image-based computation of a camera’s position and orientation with respect to a reference frame. The method aims at regular cameras, which means that 3D-3D registration methods applicable to RGB-D cameras are not an option. The tracked frame contains only 2D information, thus requiring a solution to the absolute pose or 2D-3D registration problem. While traditional solutions to camera tracking [3] rely on sparse feature correspondences, the community has recently seen a number of direct photometric registration methods such as [8] and [1]. [1] is conceptually similar to [8], however gains computational efficiency by reducing the computation from dense to semi-dense regions that correspond to a thresholded edge-map of the image. Photometric methods have the more general advantage of compensating for appearance variations caused by perspective view-point changes, whereas classical sparse methods often rely on static feature descriptors only (providing at most rotation and scale invariant properties [5, 6]). However, photometric registration techniques inherently suffer from the disability to overcome large disparities, where large sometimes means even just a couple of pixels [7]. Many photometric registration techniques therefore depend on pyramidal subsampling schemes in order to alleviate this problem. The goal of the present paper is a novel 2D-3D registration paradigm for semi-dense depth maps that relies on the Iterative Closest Point (ICP) technique, and thus a reintroduction of geometric error minimization as a valid alternative for real-time monocular camera tracking in the case of semi-dense features. An example semi-dense depth map is indicated in Figure 1. In comparison to photometric registration techniques, our ICP technique has the conceptual advantage of requiring neither isotropic enlargement of the employed semi-dense regions, nor pyramidal subsampling. The work is in line with [2], Tomono [9], and Klein and Murray [4], which already attempt curve or edge registration in 2D using ICP. Based on a hypothesized relative pose, the basic idea consists of warping a reference curve into the tracked image based on a prior 3D model or depth (in our case semi-dense) inside a reference frame. From a mathematical point of view, our idea may be formulated as follows. Let We address the problem of estimating the pose of a cam- era relative to a known 3D scene from a single RGB-D frame. We formulate this problem as inversion of the generative rendering procedure, i.e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input. This is a non-convex optimization problem with many local optima. We propose a hybrid discriminative-generative learning architecture that consists of: (i) a set of M predictors which generate M camera pose hypotheses, and (ii) a 'selector' or 'aggregator' that infers the best pose from the multiple pose hypotheses based on a similarity function. We are interested in predictors that not only produce good hypotheses but also hypotheses that are different from each other. Thus, we propose and study methods for learning 'marginally relevant' predictors, and compare their performance when used with different selection procedures. We evaluate our method on a recently released 3D reconstruction dataset with challenging camera poses, and scene variability. Experiments show that our method learns to make multiple predictions that are marginally relevant and can effectively select an accurate prediction. Furthermore, our method outperforms the state-of-the-art discriminative approach for camera relocalization. Image-based localization approaches aim to determine the camera pose from which an image was taken. Finding correct 2D-3D correspondences between query image features and 3D points in the scene model becomes harder as the size of the model increases. Current state-of-the-art methods therefore combine elaborate matching schemes with camera pose estimation techniques that are able to handle large fractions of wrong matches. In this work we study the benefits and limitations of spatial verification compared to appearance-based filtering. We propose a voting-based pose estimation strategy that exhibits O(n) complexity in the number of matches and thus facilitates to consider much more matches than previous approaches - whose complexity grows at least quadratically. This new outlier rejection formulation enables us to evaluate pose estimation for 1-to-many matches and to surpass the state-of-the-art. At the same time, we show that using more matches does not automatically lead to a better performance.