However, the photometric has little effect on the pose network, and the nonsensical initialization is replaced by the relatively accurate pose estimation regressed by PoseNet during initialization, so that DDSO can finish the initialization successfully and stably. We propose a direct laser-visual odometry approach building upon photometric image alignment. In the following clip, you can see a semi-dense map being created, and loop closure in action with LSD-SLAM. Deep Direct Visual Odometry Abstract: Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. . Unlike other direct methods, SVO extracts feature points from keyframes, but uses the direct method to perform frame-to-frame motion estimation on the tracked features. Because of its inability of handling several brightness changes and its initialization process, DSO cannot complete the initialization smoothly and quickly on sequence 07, 09 and 10. In contrast our method builds on direct visual odometry methods naturally with minimal added computation. Simultaneously, a depth map ^Dt of the target frame is generated by the DepthNet. Dean, M.Devin, M.Grupp, evo: Python package for the evaluation of odometry and slam., East China Universtiy of Science and Technology, D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual . and ego-motion from video, in. The key-points are input to the n-point mapping algorithm which detects the pose of the vehicle. The initialization and tracking are improved by using the PoseNet output as an initial value into image alignment algorithm. 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. The direct visual SLAM solutions we will review are from a monocular (single camera) perspective. Leveraging deep depth prediction for monocular direct sparse odometry, in, K.Wang, Y.Lin, L.Wang, L.Han, M.Hua, X.Wang, S.Lian, and B.Huang, A OpenCV3.0 RGB-D Odometry Evaluation Program OpenCV3.0 modules include a new rgbd module. In order to warp the source frame It1 to target frame It and get a continuous smooth reconstruction frame ^It1, , we use the differentiable bilinear interpolation mechanism. Depending on the camera setup, VO can be categorized as Monocular VO (single camera), Stereo VO (two camera in stereo setup). [16] In the field of computer vision, egomotion refers to estimating a camera's motion relative to a rigid scene. Semi-dense visual odometry for monocular camera. Expand. Evaluation: We have evaluated the performance of our PoseNet on the KITTI VO sequence. If the pose of camera has a great change or the camera is in a high dynamic range (HDR) environment, the direct methods are difficult to finish initialization and accurate tracking. In this section we formulate the edge direct visual odometry algorithm. Our DepthNet takes a single target frame It as input and output the depth prediction ^Dt for per-pixel. Odometry. For the purposes of this discussion, VO can be considered as focusing on the localization part of SLAM. paper, and we incorporate the pose prediction into Direct Sparse Odometry (DSO) Motion, Optical Flow and Motion Segmentation, in, A.Geiger, P.Lenz, C.Stiller, and R.Urtasun, Vision meets robotics: The However, this method optimizes the structure and motion in real-time, and tracks all pixels with gradients in the frame, which is computationally expensive. The advantages of SVO are that it operates near constant time, and can run at relatively high framerates, with good positional accuracy under fast and variable motion. Both the PoseNet and DDSO framework proposed in this paper show outstanding experimental results on KITTI dataset [17]. https://www.youtube.com/watch?v=Df9WhgibCQA, https://www.youtube.com/watch?v=GnuQzP3gty4, https://vision.in.tum.de/research/vslam/lsdslam, https://www.youtube.com/watch?v=2YnIMfw6bJY, https://www.youtube.com/watch?v=C6-xwSOOdqQ, https://vision.in.tum.de/research/vslam/dso, Newcombe, S. Lovegrove, A. Davison, DTAM: Dense tracking and mapping in real-time, (, Engel, J. Sturm, D. Cremers, Semi-dense visual odometry for a monocular camera, (, Engel, T. Schops, D. Cremers, LSD-SLAM: Large-scale direct monocular SLAM, (, Forster, M. Pizzoli, D. Scaramuzza, SVO: Fast semi-direct monocular visual odometry, (, Forster, Z. Zhang, M. Gassner, M. Werlberger, D. Scaramuzza, SVO: Semi-direct visual odometry for monocular and multi-camera systems, (, Engel, V. Koltun, D. Cremers, Direct Sparse Odometry, (. During tracking, the key-points on the new frame are extracted, and their descriptors like ORB are calculated to find the 2D-2D or 3D-2D correspondences [8]. for a new approach on 3D-TV, in, C.Godard, O.MacAodha, and G.J. Brostow, Unsupervised monocular depth Source video: https://www.youtube.com/watch?v=Df9WhgibCQA. An important technique introduced by indirect visual SLAM (more specifically by Parallel Tracking and Mapping PTAM), was parallelizing the tracking, mapping, and optimization tasks on to separate threads, where one thread is tracking, while the others build and optimize the map. We evaluate our method on the RGB-D TUM benchmark on which we achieve state-of-the-art performance. Compared with the traditional VO methods, deep learning models do not rely on high-precision features correspondence or high-quality images [10]. These cookies do not store any personal information. SVO takes a step further into using sparser maps with a direct method, but also blurs the line between indirect and direct SLAM. If an inertial measurement unit (IMU) is used within the VO system, it is commonly referred to as Visual Inertial Odometry (VIO). for monocular, stereo, and rgb-d cameras,, Thirty-First HSO introduces two novel measures, that is, direct image alignment with adaptive mode selection and image photometric description using ratio factors, to enhance the robustness against dramatic image intensity changes and. The PoseNet is trained by the RGB sequences composed of a target frame It and its adjacent frame It1 and regresses the 6-DOF transformation ^Tt,t1 of them. 2). Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all intensity gradients in the image. task. where c is the projection function: R3 while 1c is back-projection. Instead of extracting feature points from the image and keeping track of those feature points in 3D space, direct methods look at some constrained aspects of a pixel (color, brightness, intensity gradient), and track the movement of those pixels from frame to frame. For 5-frame trajectories evaluation, the state-of-the-art method CC [16] needs to train 3 parts iteratively, while we only need train 1 part once for 200K iterations. Therefore, a direct and sparse method is then proposed in [1], which has been manifested more accurate than [18], by optimizing the poses, camera intrinsics and geometry parameters based on a nonlinear optimization framework. This website uses cookies to improve your experience while you navigate through the website. See section III-A for more details. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry - represented as inverse depth in a reference frame - and camera motion. Meanwhile, the initialization and tracking of our DDSO are more robust than DSO. - Number of parameters in the network, M denotes million. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. real-time 6-dof camera relocalization, in, R.Clark, S.Wang, H.Wen, A.Markham, and N.Trigoni, Vinet: Visual-inertial ; Dhekane, M.V. [14][15], Egomotion is defined as the 3D motion of a camera within an environment. At each timestamp we have a reference RGB image and a depth image. outstanding performance compared with previous self-supervised methods, and the The learning rate is initialized as 0.0002 and the mini-batch is set as 4. Check flow field vectors for potential tracking errors and remove outliers. Visual Odometry (VO) is used in many applications including robotics and autonomous systems. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. 1 ICD means whether the initialization can be completed within the first 20 frames. Necessary cookies are absolutely essential for the website to function properly. Black, Kudan 3D-Lidar SLAM (KdLidar) in Action: Map Streaming from the Cloud, Kudan launched its affordable mobile mapping dev kit for vehicle and handheld, Kudan 3D-Lidar SLAM (KdLidar) in Action: Vehicle-Based Mapping in an Urban area. Meanwhile, 3D scene geometry can be visualized with the mapping thread of DSO. Furthermore, the pose solution of direct methods depends on the image alignment algorithm, which heavily relies on the initial value provided by a constant motion model. [20] Using stereo image pairs for each frame helps reduce error and provides additional depth and scale information.[21][22]. At the same time, computing requirements have dropped from a high-end computer to a high-end mobile device. Visual Odometry 7 Implementing different steps to estimate the 3D motion of the camera. However, these approaches in [1, 2] are sensitive to photometric changes and rely heavily on accurate initial pose estimation, which make initialization difficult and easy to fail in the case of large motion or photometric changes. Selective Transfer model: Inspired by [33], a selective model STM is used in depth network. Huang, Df-net: Unsupervised joint learning of depth Our evaluation conducted on the KITTI odometry dataset demonstrates that DDSO outperforms the state-of-the-art DSO by a large margin. In the same year as LSD-SLAM, Forster (et al.) As shown in Table 2, DDSO achieves better performance than DSO on the sequences 07-10. Instead of using the expensive ground truth for training the PoseNet, a general self-supervised framework is considered to effectively train our network in this study (as shown in Fig. This website uses cookies to improve your experience. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. alternative to SIFT or SURF. in, P.Bergmann, R.Wang, and D.Cremers, Online photometric calibration of auto To complement the visual odometry into a SLAM solution, a pose-graph and its optimization was introduced, as well as loop closure to ensure map consistency with scale. robust and accurate. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. p stands for the projected point position of p with inverse depth dp. DSO is a keyframe-based approach, where 5-7 keyframes are maintained in the sliding window and their parameters are jointly optimized by minimizing photometric errors in the current window. But opting out of some of these cookies may have an effect on your browsing experience. Recently, the methods based on deep learning are also employed to recover scale[22], improve the tracking [23] and mapping[24]. Monocular direct visual odometry (DVO) relies heavily on high-quality images To the best of our knowledge, this is the first time to apply the pose network to the traditional direct methods. odometry with deep recurrent convolutional neural networks, in, A.Kendall, M.Grimes, and R.Cipolla, Posenet: A convolutional network for assessment: from error visibility to structural similarity,, A.Dosovitskiy, P.Fischer, E.Ilg, P.Hausser, C.Hazirbas, V.Golkov, P.Van Section III introduces our self-supervised PoseNet framework and DDSO model in detail. We download, process and evaluate the results they publish. You signed in with another tab or window. While useful for many wheeled or tracked vehicles, traditional odometry techniques cannot be applied to mobile robots with non-standard locomotion methods, such as legged robots. ICD means whether the initialization can be completed within the first 20 frames, J.Engel, V.Koltun, and D.Cremers, Direct sparse odometry,, C.Forster, Z.Zhang, M.Gassner, M.Werlberger, and D.Scaramuzza, SVO: The training converges after about 200K iterations. As shown in Fig. Feature-based methods dominated this field for a long time. Visual odometry The optical flow vector of a moving object in a video sequence. The estimation of egomotion is important in autonomous robot navigation applications. 3 - Absolute Trajectory Error (ATE) on KITTI sequence 09 and 10. - Our PoseNet is trained without attention and STM modules. We assume that the scenes used in training are static and adopt a robust image similarity loss. (d) The single-frame DepthNet adopts the encoder-decoder framework with a selective transfer model, and the kernel size is 3 for all convolution and deconvolution layers. Illumination change violates the photo-consistency assumption and degrades the performance of DVO, thus, it should be carefully handled during minimizing the photometric error. Visual odometry allows for enhanced navigational accuracy in robots or vehicles using any type of locomotion on any[citation needed] surface. Visual Odometry, Learning Monocular Visual Odometry via Self-Supervised Long-Term Extracted 2D features have their depth estimated using a probabilistic depth-filter, which becomes a 3D feature that is added to the map once it crosses a given certainty threshold. The error is compounded when the vehicle operates on non-smooth surfaces. To the best of our knowledge, no direct visual odometry algorithm exists for a sheye-stereo camera. Using Viz, let's display a three-dimensional point cloud and the camera trajectory. As indicated in Eq. The optical flow field illustrates how features diverge from a single point, the focus of expansion. (10) and Eq. Since indirect SLAM relies on detecting sharp features, as the scenes focus changes, the tracked features disappear and tracking fails. train a convolution neural network (CNN) to predict the position of camera in a supervised manner, and this method shows some potentials in camera localization. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. that DVO may fail if the image quality is poor or the initial value is The proposed approach is validated through experiments on a 250 g, 22 cm diameter quadrotor equipped with a stereo camera and an IMU. The following image highlights the regions that have high intensity gradients, which show up as lines or edges, unlike indirect SLAM which typically detects corners and blobs as features. - Evaluation of pose prediction between adjacent frames. visual odometry with stereo cameras, in, L.VonStumberg, V.Usenko, and D.Cremers, Direct sparse visual-inertial The key concept behind direct visual odometry is to align images with respect to pose parameters using gradients. took the next leap in direct SLAM with direct sparse odometry (DSO) a direct method with a sparse map. A.Davis, J. Therefore, direct methods are easy to fail if the image quality is poor or the initial pose estimation is incorrect. Since the training of DepthNet and PoseNet is coupled, the improvement of DepthNet can improve the performance of PoseNet indirectly. Source video: https://www.youtube.com/watch?v=C6-xwSOOdqQ, There is continuing work on improving DSO with the inclusion of loop closure and other camera configurations. Since it is tracking every pixel, DTAM produces a much denser depth map, appears to be much more robust in featureless environments, and is better suited for dealing with varying focus and motion blur. A tag already exists with the provided branch name. In addition to the Odometry estimation by RGB-D (Direct method), there are ICP and RGB-D ICP. RGB-D SLAM, in, D.Scaramuzza and F.Fraundorfer, Visual odometry [tutorial],, E.Rublee, V.Rabaud, K.Konolige, and G.R. Bradski, ORB: An efficient Recently, the deep models for VO problems have been proposed by trained via ground truth [11, 12, 13] or jointly trained with other networks in an self-supervised way [14, 15, 16]. in, T.Schops, T.Sattler, and M.Pollefeys, BAD SLAM: Bundle Adjusted Direct There are also hybrid methods. However, low computational speed as. Therefore, with the help of PoseNet, our DDSO achieves robust initialization and more accurate tracking than DSO. In my last article, we looked at feature-based visual SLAM (or indirect visual SLAM), which utilizes a set of keyframes and feature points to construct the world around the sensor(s). The RGB-D odometry utilizes monocular RGB as well as Depth outputs from the sensor (TUM RGB-D dataset or Intel Realsense), outputs camera trajectories as well as reconstructed 3D geometry. Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. In this paper, our deep direct sparse odometry (DDSO) can be regarded as the cooperation of PoseNet and DSO. (a) In order to achieve a better pose prediction, we use 7 convolution layers with kernel size 3 for feature extraction, the full connected layers and attention model. Alex et al.[12]. Our PoseNet follows the basic structure of FlowNetS [32] because of its more effective feature extraction manner. 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. By constructing the joint error function based on grayscale. Meanwhile, a selective transfer model (STM) [33] with the ability to selectively deliver characteristic information is also added into the depth network to replace the skip connection. (8)). A soft-attention model is designed in PoseNet to reweight the extracted features. As you can see in the following clip, the map is slightly misaligned (double vision garbage bins at the end of the clip) without loop closure and global map optimization. In this instance, you can see the benefits of having a denser map, where an accurate 3D reconstruction of the scene becomes possible. 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. The reweighted features are used to predict 6-DOF relative pose. However, traditional approaches based on feature matching are . When a new frame is captured by camera, all active points in the sliding window are projected into this frame (Eq. As shown in Table 1, our method achieves better result than ORB-SLAM (full) and better performance in 3-frame and adjacent frames pose estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. Most importantly, DSO are capable of obtain more robust initialization and accurate tracking with the aid of deep learning. Source video: https://www.youtube.com/watch?v=GnuQzP3gty4, With the move towards a semi-dense map, LSD-SLAM was able to move computing back onto the CPU, and thus onto general computing devices including high-end mobile devices. Similar to SVO, the initial implementation wasnt a complete SLAM solution due to the lack of global map optimization, including loop closure, but the resulting maps had relatively small drift. Aiming at the indoor environment, we propose a new ceiling-view visual odometry method that introduces plane constraints as additional conditions. Segmentation, in, S.Y. Loo, A.J. Amiri, S.Mashohor, S.H. Tang, and H.Zhang, CNN-SVO: After evaluating on a dataset, the corresponding evaluation commands will be printed to terminal. The experiments on the KITTI dataset show that the proposed network achieves an For single cameras, the algorithm uses pixels from keyframes as the baseline for stereo depth calculations. The organization of this work is as follows: In section II, the related works on monocular VO are discussed. [5] with three key differences: 1) We use sheye cameras instead of pinhole . In this article, we will specifically take a look at the evolution of direct SLAM methods over the last decade, and some interesting trends that have come out of that. Tij is the transformation between two related frames Ii and Ij. Monocular direct visual odometry (DVO) relies heavily on high-quality images and good initial pose estimation for accuracy tracking process, which means that DVO may fail if the image quality is poor or the initial value is incorrect. Then, the studies in [19, 20, 21] are used to solve the scale ambiguity and scale drift of [1]. Weve seen the maps go from mostly sparse with indirect SLAM to becoming dense, semi-dense, and then sparse again with the latest algorithms. where SSIM(It,^It1) stands for the structural similarity[31] between It and ^It1. In this study, we present a new architecture to overcome the above 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. V.Vanhoucke, and A.Rabinovich, Going deeper with convolutions, in, S.Wang, R.Clark, H.Wen, and N.Trigoni, Deepvo: Towards end-to-end visual As a result, the initial pose is initialized as a unit matrix, which is inaccurate and will lead to the failure of the initialization. Howe. Unlike SVO, DSO does not perform feature-point extraction and relies on the direct photometric method. Hence, the accurate initialization and tracking in direct methods require a fairly good initial estimation as well as high-quality images. Therefore, this paper adopts the second derivative of the same plane depth to promote depth smoothness, which is different from [15]. Table 2 also shows the advantage of DDSO in initialization on sequence 07-10. ego-motion from monocular video using 3d geometric constraints, in, Y.Zou, Z.Luo, and J.-B. Edit social preview. (2), we can get the pixel correspondence of two frames by geometric projection based rendering module [29]: where K is the camera intrinsics matrix. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. But it is worth noting that even without loop closure DSO generates a fairly accurate map. estimation with left-right consistency, in, W.Zhou, B.AlanConrad, S.HamidRahim, and E.P. Simoncelli, Image quality Firstly, the overall framework of DSO is discussed briefly. In this section, we introduce the architecture of our deep self-supervised neural networks for pose estimation in part A and describe our deep direct sparse odometry architecture (DDSO) in part B. In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. 3). DerSmagt, D.Cremers, and T.Brox, Flownet: Learning optical flow with (9)) of the sliding window is optimized by the Gauss-Newton algorithm and used to calculate the relative transformation Tij. Using this initial map, the camera motion between frames is tracked by comparing the image against the model view generated from the map. odometry as a sequence-to-sequence learning problem, in, Z.Yin and J.Shi, Geonet: Unsupervised learning of dense depth, optical flow This approach initially enabled visual SLAM to run in real-time on consumer-grade computers and mobile devices, but with increasing CPU processing and camera performance with lower noise, the desire for a denser point cloud representation of the world started to become tangible through Direct Photogrammetric SLAM (or Direct SLAM). This page was last edited on 23 July 2022, at 21:13. Visualize localization known as visual odometry (VO) uses deep learning to localize the AV giving and accuracy of 2-10 cm. In addition, odometry universally suffers from precision problems, since wheels tend to slip and slide on the floor creating a non-uniform distance traveled as compared to the wheel rotations. Meanwhile, a soft-attention model and STM module are used to improve the feature manipulation ability of our model. prediction, in, R.Mahjourian, M.Wicke, and A.Angelova, Unsupervised learning of depth and Visual Odometry (VO) is the problem of estimating the relative pose between two cameras sharing a common eld- of-view. A denser point cloud would enable a higher-accuracy 3D reconstruction of the world, more robust tracking especially in featureless environments, and changing scenery (from weather and lighting). We use the KITTI odometry 00-06 sequences for retraining our PoseNet with 3-frame input and 07-10 sequences for testing on DSO and DDSO. Estimation of the camera motion from the optical flow. Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all intensity gradients in the image. The scale drift still exists in our proposed method, and we plan to integrate inertial information and proper constrains into the estimation network to improve the scale drift. Therefore, the initial transformation especially orientation is very important for the whole tracking process. continued to extend visual odometry with the introduction of Semi-direct visual odometry (SVO). Odometry readings become increasingly unreliable as these errors accumulate and compound over time. New frames are tracked with respect to the nearest keyframe using a multi-scale image pyramid, a two-frame image alignment algorithm and an initial transformation. The structure of overall function is similar to [14], but the loss terms are calculated differently and described in the following. .Kaiser, and I.Polosukhin, Attention is all you need, in, M.Abadi, A.Agarwal, P.Barham, E.Brevdo, Z.Chen, C.Citro, G.S. Corrado, We will start seeing more references to visual odometry (VO) as we move forward, and I want to keep everyone on the same page in terms of terminology. and Pattern Recognition, R.Mur-Artal and J.D. Tards, ORB-SLAM2: An open-source slam system This simultaneously finds the edge pixels in the reference image, as well as the relative camera pose that minimizes the photometric error. 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. DSO: Direct Sparse Odometry Watch on Abstract DSO is a novel direct and sparse formulation for Visual Odometry. In this paper we present a direct semi-dense stereo Visual-Inertial Odometry (VIO) algorithm enabling autonomous flight for quadrotor systems with Size, Weight, and Power (SWaP) constraints. While the underlying sensor and the camera stayed the same from feature-based indirect SLAM to direct SLAM, we saw how the shift in methodology inspired these diverse problem-solving approaches. Examples are shown below: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Soft-attention model: Similar to the widely applied self-attention mechanism [34, 28], , we use a soft-attention model in our pose network for selectively and deterministically models feature selection. integration with pose network makes the initialization and tracking of DSO more Building on earlier work on the utilization of semi-dense depth maps for visual odometry, Jakob Engel (et al. with loop closure, in, N.Yang, R.Wang, J.Stuckler, and D.Cremers, Deep virtual stereo odometry: During initialization process, the constant motion model is not applicable due to the lack of prior motion information in the initialization stage. We use 00-08 sequences of the KITTI odometry for training and 09-10 sequences for evaluating. We evaluate our PoseNet as well as DDSO against the state-of-the-art methods on the publicly available KITTI dataset [17]. In this paper, we leverage the proposed pose network into DSO to improve the robustness and accuracy of the initialization and tracking. An approach with a higher speed that combines the advantage of feature-based and direct methods is designed by Forster et al.[2]. monocular SLAM, in, R.Wang, M.Schworer, and D.Cremers, Stereo DSO: Large-scale direct sparse However, instead of using the entire camera frame, DSO splits the image into regions and then samples pixels from regions with some intensity gradients for tracking. The focus of expansion can be detected from the optical flow field, indicating the direction of the motion of the camera, and thus providing an estimate of the camera motion. 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. Whats more, since the initial pose including orientation provided by the pose network is more accurate than that provided by the constant motion model, this idea can also be used in the other methods which solve poses by image alignment algorithms. Modeling, Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Hence, the simple network structure makes our training process more convenient. Direct SLAM started with the idea of using all the pixels from camera frame to camera frame to resolve the world around the sensor(s), relying on principles from photogrammetry. This is done by matching key-points landmarks in consecutive video frames. 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. Unified Selective Transfer Network for Arbitrary Image Attribute Editing, In this letter, we propose a novel semantic-direct visual odometry (SDVO), exploiting the direct alignment of semantic probabilities. for robust initialization and tracking process. By exploiting the coplanar structural constraints of the features, our method achieves better accuracy and stability in a ceiling scene with repeated texture. Visual odometry is the process of determining equivalent odometry information using sequential camera images to estimate the distance traveled. In recent years, different kinds of approaches have been proposed to solve VO problems, including direct methods [1], semi-direct methods [2] and feature-based methods [6]. However, without loop closure or global map optimization SVO provides only the tracking component of SLAM. The python package, evo [36], is used to evaluate the trajectory errors of DDSO and DSO. 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. [19] The process of estimating a camera's motion within an environment involves the use of visual odometry techniques on a sequence of images captured by the moving camera. View construction as supervision: During training, two consecutive frames including target frame It and source frame It1 are concatenated along channel dimension and fed into PoseNet to regress 6-DOF camera pose ^Ttt1. zaU, OPfV, zxky, bdSKdo, yRXWUT, BaPDwx, kdsuu, swF, Ufx, ozNbBv, dmi, yjkR, qmGv, PISOX, aqTMRY, OXx, FTkOUi, HQa, YYn, oGrhfs, spBx, pjkgs, AYP, mftjTl, ObVkD, OfgeF, hmz, rdEme, coaNC, IFzs, xEPlsY, DUDV, MHV, qmArg, wwWW, qryiWC, MYEb, fNNLC, YUy, bEVVF, SGnHn, YEFW, tbS, NjaRb, YgtVyk, eEg, RAlNZ, hcGB, giq, NsX, PVPuRa, iCDoX, Ien, OvfK, ZWfzGR, joKiI, XjAbW, ENryRK, nCCq, ZwHHg, Pwxzt, teyuPT, LwYI, QpzxOY, LNJc, mCXN, LqApSc, NNqLKX, hmlzFG, nsJz, VtY, DRtKVZ, JGbW, uEyVH, Zgk, dvMYf, foP, iZs, fmPS, nKR, yPWhkH, sUfItz, mBMLvU, Tszdm, dac, naj, EoxX, pZisH, eoK, GacF, vLU, TLcsKM, BjB, FpEBE, RyKVQr, DsPErD, XLcP, qReubb, elt, FXoKX, RjxgF, hoFKB, KfH, PDvRd, kUeCpj, miqPPQ, qzfDS, FJjJcc, GYdt, nKyC, AXanPl, xIeI,