作为智能机器人的一项基本任务，Visual Slam在过去几十年中取得了长足的进步。但是，在高度弱质地的环境下，强大的大满贯仍然非常具有挑战性。在本文中，我们提出了一个名为RWT-Slam的新型视觉大满贯系统，以解决这个问题。我们修改LOFTR网络，该网络能够在低纹理的场景下产生密集的点匹配以生成特征描述符。为了将新功能集成到流行的Orb-Slam框架中，我们开发了功能面具，以滤除不可靠的功能并采用KNN策略来增强匹配的鲁棒性。我们还对新的描述符进行了视觉词汇，以有效地循环结束。在TUM和Openloris等各种公共数据集以及我们自己的数据中测试了由此产生的RWT-SLAM。结果显示在高度弱质地的环境下表现非常有希望。

由于其对环境变化的鲁棒性，视觉猛感的间接方法是受欢迎的。 ORB-SLAM2 \ CITE {ORBSLM2}是该域中的基准方法，但是，除非选择帧作为关键帧，否则它会消耗从未被重用的描述符。轻量级和高效，因为它跟踪相邻帧之间的关键点而不计算描述符。为此，基于稀疏光流提出了一种两个级粗到微小描述符独立的Keypoint匹配方法。在第一阶段，我们通过简单但有效的运动模型预测初始关键点对应，然后通过基于金字塔的稀疏光流跟踪鲁棒地建立了对应关系。在第二阶段，我们利用运动平滑度和末端几何形状的约束来改进对应关系。特别是，我们的方法仅计算关键帧的描述符。我们在\ texit {tum}和\ texit {icl-nuim} RGB-D数据集上测试Fastorb-Slam，并将其准确性和效率与九种现有的RGB-D SLAM方法进行比较。定性和定量结果表明，我们的方法实现了最先进的准确性，并且大约是ORB-SLAM2的两倍。

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.

Visual odometry is crucial for many robotic tasks such as autonomous exploration and path planning. Despite many progresses, existing methods are still not robust enough to dynamic illumination environments. In this paper, we present AirVO, an illumination-robust and accurate stereo visual odometry system based on point and line features. To be robust to illumination variation, we introduce the learning-based feature extraction and matching method and design a novel VO pipeline, including feature tracking, triangulation, key-frame selection, and graph optimization etc. We also employ long line features in the environment to improve the accuracy of the system. Different from the traditional line processing pipelines in visual odometry systems, we propose an illumination-robust line tracking method, where point feature tracking and distribution of point and line features are utilized to match lines. In the experiments, the proposed system is extensively evaluated in environments with dynamic illumination and the results show that it achieves superior performance to the state-of-the-art algorithms.

a) Stereo input: trajectory and sparse reconstruction of an urban environment with multiple loop closures. (b) RGB-D input: keyframes and dense pointcloud of a room scene with one loop closure. The pointcloud is rendered by backprojecting the sensor depth maps from estimated keyframe poses. No fusion is performed.

We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The enhanced descriptors can be either real-valued or binary ones. We use the proposed network to boost both hand-crafted (ORB, SIFT) and the state-of-the-art learning-based descriptors (SuperPoint, ALIKE) and evaluate them on image matching, visual localization, and structure-from-motion tasks. The results show that our method significantly improves the performance of each task, particularly in challenging cases such as large illumination changes or repetitive patterns. Our method requires only 3.2ms on desktop GPU and 27ms on embedded GPU to process 2000 features, which is fast enough to be applied to a practical system.

本文介绍了一种用于水下车辆机械手系统（UVMS）的新型视野映射方法，具有特定强调自然海底环境中的鲁棒映射。水下场景映射的先前方法通常会离线处理数据，而实时运行的现有水下SLAM方法通常会集中在本地化上而不是映射。我们的方法使用GPU加速SIFT功能在图形优化框架中构建一个特征映射。地图刻度由车辆安装的立体声相机的特征约束，我们通过将机械手系统的动态定位能力从手腕安装的Fisheye摄像机融合到地图中，以将其延伸到车辆安装摄像机的有限视点之外。我们的混合SLAM方法是在Costa rican Continental Shelf级别的自然深海环境中采用UVMS收集的挑战性图像序列，我们还在浅礁调查数据集中评估立体声的立体声。这些数据集的结果证明了我们的系统的高准确性，适合于在不同的自然海底环境中运营。

We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volume to search correspondences, we use self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images. The global receptive field provided by Transformer enables our method to produce dense matches in low-texture areas, where feature detectors usually struggle to produce repeatable interest points. The experiments on indoor and outdoor datasets show that LoFTR outperforms state-of-the-art methods by a large margin. LoFTR also ranks first on two public benchmarks of visual localization among the published methods. Code is available at our project page: https://zju3dv.github.io/loftr/.

我们提出了人类和几何重要性SLAM（HGI-SLAM），这是一种使用显着和几何特征循环封闭的新方法。循环闭合是SLAM的关键要素，具有许多已建立的方法来解决此问题。但是，使用基于几何或显着的特征，当前方法是狭窄的。我们将他们的成功合并为一个模型，该模型仅优于两种类型的方法。我们的方法利用廉价的单眼相机，不依赖于深度传感器或LIDAR。 HGI-SLAM利用几何和显着特征，将它们处理成描述符，并将其优化为一袋单词算法。通过使用并发线程并将我们的环闭合检测与Orb-Slam2梳理，我们的系统是一个完整的SLAM框架。我们对Kitti和Euroc数据集进行了HGI循环检测和HGI-SLAM的广泛评估。我们还对我们的功能进行定性分析。我们的方法是实时运行的，并且在有机环境中保持准确的方式对巨大的观点变化是可靠的。 HGI-SLAM是一种端到端的大满贯系统，仅需要单眼视觉，并且在性能上与最先进的SLAM方法相当。

在这项研究中，我们提出了一种新型的视觉定位方法，以根据RGB摄像机的可视数据准确估计机器人在3D激光镜头内的六个自由度（6-DOF）姿势。使用基于先进的激光雷达的同时定位和映射（SLAM）算法，可获得3D地图，能够收集精确的稀疏图。将从相机图像中提取的功能与3D地图的点进行了比较，然后解决了几何优化问题，以实现精确的视觉定位。我们的方法允许使用配备昂贵激光雷达的侦察兵机器人一次 - 用于映射环境，并且仅使用RGB摄像头的多个操作机器人 - 执行任务任务，其本地化精度高于常见的基于相机的解决方案。该方法在Skolkovo科学技术研究所（Skoltech）收集的自定义数据集上进行了测试。在评估本地化准确性的过程中，我们设法达到了厘米级的准确性；中间翻译误差高达1.3厘米。仅使用相机实现的确切定位使使用自动移动机器人可以解决需要高度本地化精度的最复杂的任务。

我们提出了一种新颖的方法，可以可靠地估计相机的姿势，并在极端环境中获得的一系列图像，例如深海或外星地形。在这些挑战性条件下获得的数据被无纹理表面，图像退化以及重复性和高度模棱两可的结构所破坏。当天真地部署时，最先进的方法可能会在我们的经验分析确认的那些情况下失败。在本文中，我们试图在这些极端情况下使摄像机重新定位起作用。为此，我们提出：（i）一个分层定位系统，我们利用时间信息和（ii）一种新颖的环境感知图像增强方法来提高鲁棒性和准确性。我们广泛的实验结果表明，在两个极端环境下我们的方法有利于我们的方法：将自动的水下车辆定位，并将行星漫游者定位在火星样的沙漠中。此外，我们的方法仅使用20％的培训数据就可以在室内基准（7片数据集）上使用最先进的方法（7片数据集）实现可比性的性能。

尽管提取了通过手工制作和基于学习的描述符实现的本地特征的进步，但它们仍然受到不符合非刚性转换的不变性的限制。在本文中，我们提出了一种计算来自静止图像的特征的新方法，该特征对于非刚性变形稳健，以避免匹配可变形表面和物体的问题。我们的变形感知当地描述符，命名优惠，利用极性采样和空间变压器翘曲，以提供旋转，尺度和图像变形的不变性。我们通过将等距非刚性变形应用于模拟环境中的对象作为指导来提供高度辨别的本地特征来培训模型架构端到端。该实验表明，我们的方法优于静止图像中的实际和现实合成可变形对象的不同数据集中的最先进的手工制作，基于学习的图像和RGB-D描述符。描述符的源代码和培训模型在https://www.verlab.dcc.ufmg.br/descriptors/neUrips2021上公开可用。

从单眼视频中估算移动摄像头的姿势是一个具有挑战性的问题，尤其是由于动态环境中移动对象的存在，在动态环境中，现有摄像头姿势估计方法的性能易于几何一致的像素。为了应对这一挑战，我们为视频提供了一种强大的密度间接结构，该结构是基于由成对光流初始化的致密对应的。我们的关键想法是将远程视频对应性优化为密集的点轨迹，并使用它来学习对运动分割的强大估计。提出了一种新型的神经网络结构来处理不规则的点轨迹数据。然后，在远程点轨迹的一部分中，通过全局捆绑式调整估算和优化摄像头姿势，这些轨迹被归类为静态。 MPI Sintel数据集的实验表明，与现有最新方法相比，我们的系统产生的相机轨迹明显更准确。此外，我们的方法能够在完全静态的场景上保留相机姿势的合理准确性，该场景始终优于端到端深度学习的强大最新密度对应方法，这证明了密集间接方法的潜力基于光流和点轨迹。由于点轨迹表示是通用的，因此我们进一步介绍了具有动态对象的复杂运动的野外单眼视频的比较。代码可在https://github.com/bytedance/particle-sfm上找到。

Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction. At present, it still has some deficiencies needing further improvement, mainly including the discrimination power of extracted local descriptors, the localization accuracy of detected keypoints, and the efficiency of local feature learning. This paper focuses on promoting the currently popular sparse local feature learning with camera pose supervision. Therefore, it pertinently proposes a Shared Coupling-bridge scheme with four light-weight yet effective improvements for weakly-supervised local feature (SCFeat) learning. It mainly contains: i) the \emph{Feature-Fusion-ResUNet Backbone} (F2R-Backbone) for local descriptors learning, ii) a shared coupling-bridge normalization to improve the decoupling training of description network and detection network, iii) an improved detection network with peakiness measurement to detect keypoints and iv) the fundamental matrix error as a reward factor to further optimize feature detection training. Extensive experiments prove that our SCFeat improvement is effective. It could often obtain a state-of-the-art performance on classic image matching and visual localization. In terms of 3D reconstruction, it could still achieve competitive results. For sharing and communication, our source codes are available at https://github.com/sunjiayuanro/SCFeat.git.

在这项工作中，我们探讨了对物体在看不见的世界中同时本地化和映射中的使用，并提出了一个对象辅助系统（OA-Slam）。更确切地说，我们表明，与低级点相比，物体的主要好处在于它们的高级语义和歧视力。相反，要点比代表对象（Cuboid或椭圆形）的通用粗模型具有更好的空间定位精度。我们表明，将点和对象组合非常有趣，可以解决相机姿势恢复的问题。我们的主要贡献是：（1）我们使用高级对象地标提高了SLAM系统的重新定位能力； （2）我们构建了一个能够使用3D椭圆形识别，跟踪和重建对象的自动系统； （3）我们表明，基于对象的本地化可用于重新初始化或恢复相机跟踪。我们的全自动系统允许对象映射和增强姿势跟踪恢复，我们认为这可以极大地受益于AR社区。我们的实验表明，可以从经典方法失败的视点重新定位相机。我们证明，尽管跟踪损失损失，但这种本地化使SLAM系统仍可以继续工作，而这种损失可能会经常发生在不理会的用户中。我们的代码和测试数据在gitlab.inria.fr/tangram/oa-slam上发布。

We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.

In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization. Prior art has tackled each of these components individually, purportedly aiming to alleviate difficulties in effectively train a holistic network. We design a self-supervised image warping correspondence loss for both feature detection and matching, a weakly-supervised epipolar constraints loss on relative camera pose learning, and a directional matching scheme that detects key-point features in a source image and performs coarse-to-fine correspondence search on the target image. We leverage this framework to enforce cycle consistency in our matching module. In addition, we propose a new loss to robustly handle both definite inlier/outlier matches and less-certain matches. The integration of these learning mechanisms enables end-to-end training of a single network performing all three localization components. Bench-marking our approach on public data-sets, exemplifies how such an end-to-end framework is able to yield more accurate localization that out-performs both traditional methods as well as state-of-the-art weakly supervised methods.

特征提取和匹配是许多计算机视觉任务的基本部分，例如2D或3D对象检测，识别和注册。众所周知，2D功能提取和匹配已经取得了巨大的成功。不幸的是，在3D领域，由于描述性和效率低下，目前的方法无法支持3D激光雷达传感器在视觉任务中的广泛应用。为了解决此限制，我们提出了一种新颖的3D特征表示方法：3D激光点云的线性关键点表示，称为link3d。 Link3D的新颖性在于它完全考虑了LiDar Point Cloud的特征（例如稀疏性，场景的复杂性），并用其强大的邻居键盘来表示当前关键点，从而对当前关键点的描述提供了强烈的约束。提出的链接3D已在两个公共数据集（即Kitti，Steven VLP16）上进行了评估，实验结果表明，我们的方法在匹配性能方面的最先进表现都大大优于最先进的方法。更重要的是，Link3D显示出出色的实时性能（基于LIDAR的频率10 Hz）。 Link3D平均仅需32毫秒即可从64射线激光束收集的点云中提取功能，并且仅需大约8毫秒即可匹配两次LIDAR扫描，当时用Intel Core i7 @2.2 GHz处理器执行笔记本。此外，我们的方法可以广泛扩展到各种3D视觉应用。在本文中，我们已将Link3D应用于3D注册，LiDAR ODOMETIRE和放置识别任务，并与最先进的方法相比实现了竞争成果。

在本文中，我们提出了一个紧密耦合的视觉惯性对象级多效性动态大满贯系统。即使在极其动态的场景中，它也可以为摄像机姿势，速度，IMU偏见并构建一个密集的3D重建对象级映射图。我们的系统可以通过稳健的传感器和对象跟踪，可以强牢固地跟踪和重建任意对象的几何形状，其语义和运动的几何形状，其语义和运动的几何形状，并通过逐步融合相关的颜色，深度，语义和前景对象概率概率。此外，当对象在视野视野外丢失或移动时，我们的系统可以在重新观察时可靠地恢复其姿势。我们通过定量和定性测试现实世界数据序列来证明我们方法的鲁棒性和准确性。

This paper presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models.The first main novelty is a feature-based tightly-integrated visual-inertial SLAM system that fully relies on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization phase. The result is a system that operates robustly in real time, in small and large, indoor and outdoor environments, and is two to ten times more accurate than previous approaches.The second main novelty is a multiple map system that relies on a new place recognition method with improved recall. Thanks to it, ORB-SLAM3 is able to survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information. This allows to include in bundle adjustment co-visible keyframes, that provide high parallax observations boosting accuracy, even if they are widely separated in time or if they come from a previous mapping session.Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.5 cm in the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting representative of AR/VR scenarios. For the benefit of the community we make public the source code.