In this paper we propose an edge-direct visual odometry algorithm that efficiently utilizes edge pixels to find the relative pose that minimizes the photometric error between images. Prior work on exploiting edge pixels instead treats edges as features and employ various techniques to match edge lines or pixels, which adds unnecessary complexity. Direct methods typically operate on all pixel intensities, which proves to be highly redundant. In contrast our method builds on direct visual odometry methods naturally with minimal added computation. It is not only more efficient than direct dense methods since we iterate with a fraction of the pixels, but also more accurate. We achieve high accuracy and efficiency by extracting edges from only one image, and utilize robust Gauss-Newton to minimize the photometric error of these edge pixels. This simultaneously finds the edge pixels in the reference image, as well as the relative camera pose that minimizes the photometric error. We test various edge detectors, including learned edges, and determine that the optimal edge detector for this method is the Canny edge detection algorithm using automatic thresholding. We highlight key differences between our edge direct method and direct dense methods, in particular how higher levels of image pyramids can lead to significant aliasing effects and result in incorrect solution convergence. We show experimentally that reducing the photometric error of edge pixels also reduces the photometric error of all pixels, and we show through an ablation study the increase in accuracy obtained by optimizing edge pixels only. We evaluate our method on the RGB-D TUM benchmark on which we achieve state-of-the-art performance.
There have been many iterations of direct dense methods such as direct dense VO in @cite_1 , RGB-D SLAM @cite_22 , and LSD-SLAM @cite_25 . Even using dense methods, these systems achieve real-time performance on modern CPUs due to the highly efficient nature of these types of algorithms. More recent advances highlight the fact that the information contained in image intensities are highly redundant, and attempt to minimize the photometric error only over sparse random points in the image in order to increase efficiency and thus speed @cite_10 . Another direct method that has been used with success is the iterative closest point (ICP) algorithm, which is used in systems such as @cite_8 @cite_19 . These systems minimize the difference between point alignment in contrast to image intensities.
@cite_22 @cite_8 @cite_1 @cite_19 @cite_10 @cite_25
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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. Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images. These features, while suitable for sparse mapping, often lead to ambiguous matches at texture-less regions when performing dense reconstruction due to the aperture problem. In this work, we explore the use of learned features for the matching task in dense monocular reconstruction. We propose a novel convolutional neural network (CNN) architecture along with a deeply supervised feature learning scheme for pixel-wise regression of visual descriptors from an image which are best suited for dense monocular SLAM. In particular, our learning scheme minimizes a multi-view matching cost-volume loss with respect to the regressed features at multiple stages within the network, for explicitly learning contextual features that are suitable for dense matching between images captured by a moving monocular camera along the epipolar line. We utilize the learned features from our model for depth estimation inside a real-time dense monocular SLAM framework, where photometric error is replaced by our learned descriptor error. Our evaluation on several challenging indoor scenes demonstrate greatly improved accuracy in dense reconstructions of the well celebrated dense SLAM systems like DTAM, without compromising their real-time performance. We present a new simultaneous localization and mapping SLAM system capable of producing high-quality globally consistent surface reconstructions over hundreds of meters in real time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every-frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an 'as-rigid-as-possible' space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems. This paper proposes a new visual SLAM technique that not only integrates 6 degrees of freedom (DOF) pose and dense structure but also simultaneously integrates the colour information contained in the images over time. This involves developing an inverse model for creating a super-resolution map from many low resolution images. Contrary to classic super-resolution techniques, this is achieved here by taking into account full 3D translation and rotation within a dense localisation and mapping framework. This not only allows to take into account the full range of image deformations but also allows to propose a novel criteria for combining the low resolution images together based on the difference in resolution between different images in 6D space. Another originality of the proposed approach with respect to the current state of the art lies in the minimisation of both colour (RGB) and depth (D) errors, whilst competing approaches only minimise geometry. Several results are given showing that this technique runs in real-time (30Hz) and is able to map large scale environments in high-resolution whilst simultaneously improving the accuracy and robustness of the tracking.