在本文中，引入了一种新颖的解决方案，用于由深度学习组件构建的视觉同时定位和映射（VSLAM）。所提出的体系结构是一个高度模块化的框架，在该框架中，每个组件在基于视觉的深度学习解决方案的领域中提供了最新的最新技术。该论文表明，通过这些单个构建基块的协同整合，可以创建一个功能高效，有效的全直神经（ATDN）VSLAM系统。引入了嵌入距离损耗函数并使用ATDN体系结构进行了训练。最终的系统在Kitti数据集的子集上设法实现了4.4％的翻译和0.0176 ver/m的旋转误差。所提出的体系结构可用于有效，低延迟的自主驾驶（AD）协助数据库创建以及自动驾驶汽车（AV）控制的基础。

作为许多自主驾驶和机器人活动的基本组成部分，如自我运动估计，障碍避免和场景理解，单眼深度估计（MDE）引起了计算机视觉和机器人社区的极大关注。在过去的几十年中，已经开发了大量方法。然而，据我们所知，对MDE没有全面调查。本文旨在通过审查1970年至2021年之间发布的197个相关条款来弥补这一差距。特别是，我们为涵盖各种方法的MDE提供了全面的调查，介绍了流行的绩效评估指标并汇总公开的数据集。我们还总结了一些代表方法的可用开源实现，并比较了他们的表演。此外，我们在一些重要的机器人任务中审查了MDE的应用。最后，我们通过展示一些有希望的未来研究方向来结束本文。预计本调查有助于读者浏览该研究领域。

In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode will be available as open-source software under https://github.com/TUMFTM/ORB SLAM3 RGBL.

同时本地化和映射（SLAM）是自动移动机器人中的基本问题之一，在该机器人需要重建以前看不见的环境的同时，同时在地图上进行了本身。特别是，Visual-Slam使用移动机器人中的各种传感器来收集和感测地图的表示。传统上，基于几何模型的技术被用来解决大满贯问题，在充满挑战的环境下，该问题往往容易出错。诸如深度学习技术之类的计算机视觉方面的最新进展提供了一种数据驱动的方法来解决视觉范围问题。这篇综述总结了使用各种基于学习的方法的视觉 - 峰领域的最新进展。我们首先提供了基于几何模型的方法的简洁概述，然后进行有关SLAM当前范式的技术评论。然后，我们介绍了从移动机器人那里收集感官输入并执行场景理解的各种基于学习的方法。讨论并将基于深度学习的语义理解中的当前范式讨论并置于视觉峰的背景下。最后，我们讨论了在视觉 - 峰中基于学习的方法方向上的挑战和进一步的机会。

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。

Simultaneous Localization & Mapping (SLAM) is the process of building a mutual relationship between localization and mapping of the subject in its surrounding environment. With the help of different sensors, various types of SLAM systems have developed to deal with the problem of building the relationship between localization and mapping. A limitation in the SLAM process is the lack of consideration of dynamic objects in the mapping of the environment. We propose the Dynamic Object Tracking SLAM (DyOb-SLAM), which is a Visual SLAM system that can localize and map the surrounding dynamic objects in the environment as well as track the dynamic objects in each frame. With the help of a neural network and a dense optical flow algorithm, dynamic objects and static objects in an environment can be differentiated. DyOb-SLAM creates two separate maps for both static and dynamic contents. For the static features, a sparse map is obtained. For the dynamic contents, a trajectory global map is created as output. As a result, a frame to frame real-time based dynamic object tracking system is obtained. With the pose calculation of the dynamic objects and camera, DyOb-SLAM can estimate the speed of the dynamic objects with time. The performance of DyOb-SLAM is observed by comparing it with a similar Visual SLAM system, VDO-SLAM and the performance is measured by calculating the camera and object pose errors as well as the object speed error.

结合同时定位和映射（SLAM）估计和动态场景建模可以高效地在动态环境中获得机器人自主权。机器人路径规划和障碍避免任务依赖于场景中动态对象运动的准确估计。本文介绍了VDO-SLAM，这是一种强大的视觉动态对象感知SLAM系统，用于利用语义信息，使得能够在场景中进行准确的运动估计和跟踪动态刚性物体，而无需任何先前的物体形状或几何模型的知识。所提出的方法识别和跟踪环境中的动态对象和静态结构，并将这些信息集成到统一的SLAM框架中。这导致机器人轨迹的高度准确估计和对象的全部SE（3）运动以及环境的时空地图。该系统能够从对象的SE（3）运动中提取线性速度估计，为复杂的动态环境中的导航提供重要功能。我们展示了所提出的系统对许多真实室内和室外数据集的性能，结果表明了对最先进的算法的一致和实质性的改进。可以使用源代码的开源版本。

摄像机是自动化驱动系统中的主要传感器。它们提供高信息密度，并对检测为人类视野提供的道路基础设施线索最优。环绕式摄像机系统通常包括具有190 {\ DEG} +视野的四个鱼眼相机，覆盖在车辆周围的整个360 {\ DEG}集中在近场传感上。它们是低速，高精度和近距离传感应用的主要传感器，如自动停车，交通堵塞援助和低速应急制动。在这项工作中，我们提供了对这种视觉系统的详细调查，在可以分解为四个模块化组件的架构中，设置调查即可识别，重建，重建和重组。我们共同称之为4R架构。我们讨论每个组件如何完成特定方面，并提供一个位置论证，即它们可以协同组织以形成用于低速自动化的完整感知系统。我们通过呈现来自以前的作品的结果，并通过向此类系统提出架构提案来支持此参数。定性结果在视频中呈现在HTTPS://youtu.be/ae8bcof7777uy中。

在本文中，我们串联串联一个实时单手抄语和密集的测绘框架。对于姿势估计，串联基于关键帧的滑动窗口执行光度束调整。为了增加稳健性，我们提出了一种新颖的跟踪前端，使用从全局模型中呈现的深度图来执行密集的直接图像对齐，该模型从密集的深度预测逐渐构建。为了预测密集的深度映射，我们提出了通过分层构造具有自适应视图聚合的3D成本卷来平衡关键帧之间的不同立体声基线的3D成本卷来使用整个活动密钥帧窗口的级联视图 - 聚合MVSNet（CVA-MVSNET）。最后，将预测的深度映射融合到表示为截短的符号距离函数（TSDF）体素网格的一致的全局映射中。我们的实验结果表明，在相机跟踪方面，串联优于其他最先进的传统和学习的单眼视觉径管（VO）方法。此外，串联示出了最先进的实时3D重建性能。

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.

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.

我们提出了场景运动的新颖双流表示，将光流分​​解为由摄像机运动引起的静态流场和另一个由场景中对象的运动引起的动态流场。基于此表示形式，我们提出了一个动态的大满贯，称为Deflowslam，它利用图像中的静态和动态像素来求解相机的姿势，而不是像其他动态SLAM系统一样简单地使用静态背景像素。我们提出了一个动态更新模块，以一种自我监督的方式训练我们的Deflowslam，其中密集的束调节层采用估计的静态流场和由动态掩码控制的权重，并输出优化的静态流动场的残差，相机姿势的残差，和反度。静态和动态流场是通过将当前图像翘曲到相邻图像来估计的，并且可以通过将两个字段求和来获得光流。广泛的实验表明，在静态场景和动态场景中，Deflowslam可以很好地推广到静态和动态场景，因为它表现出与静态和动态较小的场景中最先进的Droid-Slam相当的性能，同时在高度动态的环境中表现出明显优于Droid-Slam。代码和数据可在项目网页上找到：\ urlstyle {tt} \ textColor {url_color} {\ url {https://zju3dv.github.io/deflowslam/}}}。

随着自动驾驶行业正在缓慢成熟，视觉地图本地化正在迅速成为尽可能准确定位汽车的标准方法。由于相机或激光镜等视觉传感器返回的丰富数据，研究人员能够构建具有各种细节的不同类型的地图，并使用它们来实现高水平的车辆定位准确性和在城市环境中的稳定性。与流行的SLAM方法相反，视觉地图本地化依赖于预先构建的地图，并且仅通过避免误差积累或漂移来提高定位准确性。我们将视觉地图定位定义为两个阶段的过程。在位置识别的阶段，通过将视觉传感器输出与一组地理标记的地图区域进行比较，可以确定车辆在地图中的初始位置。随后，在MAP指标定位的阶段，通过连续将视觉传感器的输出与正在遍历的MAP的当前区域进行对齐，对车辆在地图上移动时进行了跟踪。在本文中，我们调查，讨论和比较两个阶段的基于激光雷达，基于摄像头和跨模式的视觉图本地化的最新方法，以突出每种方法的优势。

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and egomotion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.

Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms. Compared to similar systems, we utilize raw depth maps for odometry computation and loop closure refinement which results in better reconstructions. We acquire a building-scale 3D dataset (BS3D) and demonstrate its value by training an improved monocular depth estimation model. As a unique experiment, we benchmark visual-inertial odometry methods using both color and active infrared images.

在接受高质量的地面真相（如LiDAR数据）培训时，监督的学习深度估计方法可以实现良好的性能。但是，LIDAR只能生成稀疏的3D地图，从而导致信息丢失。每个像素获得高质量的地面深度数据很难获取。为了克服这一限制，我们提出了一种新颖的方法，将有前途的平面和视差几何管道与深度信息与U-NET监督学习网络相结合的结构信息结合在一起，与现有的基于流行的学习方法相比，这会导致定量和定性的改进。特别是，该模型在两个大规模且具有挑战性的数据集上进行了评估：Kitti Vision Benchmark和CityScapes数据集，并在相对错误方面取得了最佳性能。与纯深度监督模型相比，我们的模型在薄物体和边缘的深度预测上具有令人印象深刻的性能，并且与结构预测基线相比，我们的模型的性能更加强大。

The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. 1

现代计算机视觉已超越了互联网照片集的领域，并进入了物理世界，通过非结构化的环境引导配备摄像头的机器人和自动驾驶汽车。为了使这些体现的代理与现实世界对象相互作用，相机越来越多地用作深度传感器，重建了各种下游推理任务的环境。机器学习辅助的深度感知或深度估计会预测图像中每个像素的距离。尽管已经在深入估算中取得了令人印象深刻的进步，但仍然存在重大挑战：（1）地面真相深度标签很难大规模收集，（2）通常认为相机信息是已知的，但通常是不可靠的，并且（3）限制性摄像机假设很常见，即使在实践中使用了各种各样的相机类型和镜头。在本论文中，我们专注于放松这些假设，并描述将相机变成真正通用深度传感器的最终目标的贡献。

在现有方法中，LIDAR的探测器显示出卓越的性能，但视觉探测器仍被广泛用于其价格优势。从惯例上讲，视觉检验的任务主要依赖于连续图像的输入。但是，探测器网络学习图像提供的异性几何信息非常复杂。在本文中，将伪LIDAR的概念引入了探测器中以解决此问题。伪LIDAR点云背面项目由图像生成的深度图中的3D点云，这改变了图像表示的方式。与立体声图像相比，立体声匹配网络生成的伪lidar点云可以得到显式的3D坐标。由于在3D空间中发生了6个自由度（DOF）姿势转换，因此伪宽点云提供的3D结构信息比图像更直接。与稀疏的激光雷达相比，伪驱动器具有较密集的点云。为了充分利用伪LIDAR提供的丰富点云信息，采用了投射感知的探测管道。以前的大多数基于激光雷达的算法从点云中采样了8192点，作为探视网络的输入。投影感知的密集探测管道采用从图像产生的所有伪lidar点云，除了误差点作为网络的输入。在图像中充分利用3D几何信息时，图像中的语义信息也用于探视任务中。 2D-3D的融合是在仅基于图像的进程中实现的。 Kitti数据集的实验证明了我们方法的有效性。据我们所知，这是使用伪LIDAR的第一种视觉探光法。

Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge, still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.