lightweight；lidar；slam_固态激光雷达退化

汇总：

以上述三个为关键词搜索，共汇总顶会顶刊13篇，大致分为以下几种：

1.有离线地图的定位---2篇

2.语义信息辅助定位，解决耗时问题---1篇

3.基于激光雷达的位置识别问题---3篇

4.固态激光雷达的框架---1篇

5.因子图紧耦合视觉、惯性和激光雷达---1篇

6.解决固态激光雷达在室内场景下的退化问题---1篇

7.改进存储数据的空间结构，提升速度---1篇

8.通过关键帧策略 利用密集点云进行里程计估计---1篇

9.通过EKF融合，解决机器人在几何特征少的环境中距离低估问题---1篇

10.神经网络解决跨场景问题---1篇

1.Stereo Camera Localization in 3D LiDAR Maps

IROS-2018

激光点云地图的基础上运用双目进行定位

        In this paper, we propose a novel and lightweight camera-only visual positioning algorithm that involves localization within prior 3D LiDAR maps. We aim to achieve the consumer level global positioning system (GPS) accuracy using vision within the urban environment, where GPS signal is unreliable. Via exploiting a stereo camera, depth from the stereo disparity map is matched with 3D LiDAR maps. A full six degree of freedom (DOF) camera pose is estimated via minimizing depth residual. Powered by visual tracking that provides a good initial guess for the localization, the proposed depth residual is successfully applied for camera pose estimation. Our method runs online, as the average localization error is comparable to ones resulting from state-of-the-art approaches. We validate the proposed method as a stand-alone localizer using KITTI dataset and as a module in the SLAM framework using our own dataset.

        在本文中，我们提出了一种新颖且轻量级的仅相机视觉定位算法，该算法涉及在先前的 3D LiDAR 地图中进行定位。我们的目标是在 GPS 信号不可靠的城市环境中使用视觉实现消费者级别的全球定位系统 (GPS) 精度。通过利用立体相机，立体视差图的深度与 3D LiDAR 地图相匹配。通过最小化深度残差来估计完整的六自由度 (DOF) 相机位姿。在为定位提供良好初始猜测的视觉跟踪的支持下，所提出的深度残差成功地应用于相机姿态估计。我们的方法在线运行，因为平均定位误差与最先进方法产生的误差相当。我们使用 KITTI 数据集将所提出的方法验证为独立的定位器，并使用我们自己的数据集验证为 SLAM 框架中的模块。

2.Lidar Mapping Optimization Based on Lightweight Semantic Segmentation

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES--2019

利用语义分割网络获取的语义信息辅助SLAM ，主要解决神经网络的耗时问题，进行轻量化处理

        Lidar Simultaneous Localization and Mapping (LiDAR-SLAM) algorithm with semantic information is an open research question and it is such a time consuming task. The related work that focus on real-time LiDAR-SLAM has poor accuracy. To solve these problems, a lightweight semantic segmentation network to assist in localization and mapping is proposed in this paper. The method uses the lidar point clouds generated by the simulator and annotated manually in real world as the original input. Then, the semantic cloud is segmented by the semantic segmentation network to obtain the semantic information. Finally, the semantic information obtained by the segmentation is used to assist the localization and mapping. 

        具有语义信息的激光雷达同步定位和映射 (LiDAR-SLAM) 算法是一个开放的研究问题，并且是一项耗时的任务。专注于实时 LiDAR-SLAM 的相关工作准确性较差。为了解决这些问题，本文提出了一种辅助定位和建图的轻量级语义分割网络。该方法使用模拟器生成并在现实世界中手动注释的激光雷达点云作为原始输入。然后，通过语义分割网络对语义云进行分割，得到语义信息。最后将分割得到的语义信息用于辅助定位和建图。

3.Versatile 3D Multi-Sensor Fusion for Lightweight 2D Localization

IROS-2020

两阶段定位系统，结合离线先验地图和在线多模态定位（预先构建的占据网格地图、IMU、里程计、2D激光雷达测量值）

        Aiming for a lightweight and robust localization solution for low-cost, low-power autonomous robot platforms, such as educational or industrial ground vehicles, under challenging conditions (e.g., poor sensor calibration, low lighting and dynamic objects), we propose a two-stage localization system which incorporates both offline prior map building and online multi-modal localization. In particular, we develop an occupancy grid mapping system with probabilistic odometry fusion, accurate scan-to-submap covariance modeling, and accelerated loop-closure detection, which is further aided by 2D line features that exploit the environmental structural constraints. We then develop a versatile EKF-based online localization system which optimally (up to linearization) fuses multi-modal information provided by the pre-built occupancy grid map, IMU, odometry, and 2D LiDAR measurements with low computational requirements. Importantly, spatiotemporal calibration between these sensors are also estimated online to account for poor initial calibration and make the system more "plug-and-play", which improves both the accuracy and flexibility of the proposed multi-sensor fusion framework. In our experiments, our mapping system is shown to be more accurate than the state-of-the-art Google Cartographer. Then, extensive Monte-Carlo simulations are performed to verify both accuracy, consistency and efficiency of the proposed map-based localization system with full spatiotemporal calibration. We also validate the complete system (prior map building and online localization) with building-scale real-world datasets.

        为了在具有挑战性的条件下（例如，传感器校准差、光照不足和动态物体）为低成本、低功耗的自主机器人平台（例如教育或工业地面车辆）提供轻量级和强大的定位解决方案，我们提出了一个两阶段定位系统，它结合了离线先验地图构建和在线多模式定位。特别是，我们开发了一个具有概率测距融合、精确的扫描到子图协方差建模和加速闭环检测的占用网格映射系统，这进一步得到了利用环境结构约束的 2D 线特征的帮助。然后，我们开发了一个多功能的基于 EKF 的在线定位系统，该系统以最佳方式（直至线性化）融合了由预先构建的占用网格图、IMU、里程计和 2D LiDAR 测量提供的多模态信息，计算要求低。重要的是，这些传感器之间的时空校准也是在线估计的，以解决初始校准不佳的问题，并使系统更加“即插即用”，从而提高了所提出的多传感器融合框架的准确性和灵活性。在我们的实验中，我们的地图系统被证明比最先进的 Google Cartographer 更准确。然后，进行了广泛的蒙特卡罗模拟，以验证所提出的基于地图的定位系统的准确性、一致性和效率，并具有全时空校准。我们还使用建筑规模的真实世界数据集验证了完整的系统（先前的地图构建和在线本地化）。

4.DiSCO: Differentiable Scan Context With Orientation

 IEEE ROBOTICS AND AUTOMATION LETTERS--2021

基于激光雷达的位置识别，将位置的特征转换为频域，利用频谱的幅度作为位置描述符，使用频谱有效地估计全局最优相对方向。

        Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this letter, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation. The orientation can further be used as the initial value for the down-stream local optimal metric pose estimation, improving the pose estimation especially when a large orientation between the current scan and retrieved scan exists. Our key idea is to transform the feature into the frequency domain. We utilize the magnitude of the spectrum as the place descriptor, which is theoretically rotation-invariant. In addition, based on the differentiable phase correlation, we can efficiently estimate the global optimal relative orientation using the spectrum. With such structural constraints, the network can be learned in an end-to-end manner, and the backbone is fully shared by the two tasks, achieving better interpretability and lightweight. Finally, DiSCO is validated on three datasets with long-term outdoor conditions, showing better performance than the compared methods. Codes are released at https://github.com/MaverickPeter/DiSCO-pytorch.

        全局定位对于机器人导航至关重要，其中第一步是从地图数据库中检索查询。这个问题称为地点识别。近年来，基于激光雷达扫描的位置识别因其对外观变化的鲁棒性而引起了人们的关注。在这封信中，我们提出了一种基于 LiDAR 的位置识别方法，称为带方向的可微扫描上下文 (DiSCO)，它同时在相似位置找到扫描并估计它们的相对方向。该方向可以进一步用作下游局部最优度量姿态估计的初始值，特别是当当前扫描和检索到的扫描之间存在大方向时，改进了姿态估计。我们的关键思想是将特征转换为频域。我们利用频谱的幅度作为位置描述符，理论上它是旋转不变的。此外，基于可微分的相位相关性，我们可以使用频谱有效地估计全局最优相对方向。有了这样的结构约束，网络可以以端到端的方式学习，并且两个任务完全共享主干，实现更好的可解释性和轻量级。最后，DiSCO 在三个具有长期户外条件的数据集上进行了验证，显示出比比较方法更好的性能。代码在 https://github.com/MaverickPeter/DiSCO-pytorch 发布。

5. Lightweight 3-D Localization and Mapping for Solid-State LiDAR

  IEEE ROBOTICS AND AUTOMATION LETTERS--2021

针对固态激光雷达的新的框架，包含特征提取、里程计估计和概率地图的构建。

        The LIght Detection And Ranging (LiDAR) sensor has become one of the most important perceptual devices due to its important role in simultaneous localization and mapping (SLAM). Existing SLAM methods are mainly developed for mechanical LiDAR sensors, which are often adopted by large scale robots. Recently, the solid-state LiDAR is introduced and becomes popular since it provides a cost-effective and lightweight solution for small scale robots. Compared to mechanical LiDAR, solid-state LiDAR sensors have higher update frequency and angular resolution, but also have smaller field of view (FoV), which is very challenging for existing LiDAR SLAM algorithms. Therefore, it is necessary to have a more robust and computationally efficient SLAM method for this new sensing device. To this end, we propose a new SLAM framework for solid-state LiDAR sensors, which involves feature extraction, odometry estimation, and probability map building. The proposed method is evaluated on a warehouse robot and a hand-held device. In the experiments, we demonstrate both the accuracy and efficiency of our method using an Intel L515 solid-state LiDAR. The results show that our method is able to provide precise localization and high quality mapping. We made the source codes public at https://github.com/wh200720041/SSL_SLAM.

         光探测与测距 (LiDAR) 传感器因其在同步定位与建图 (SLAM) 中的重要作用而成为最重要的感知设备之一。现有的 SLAM 方法主要是针对机械 LiDAR 传感器开发的，这些传感器通常被大型机器人采用。最近，固态激光雷达被引入并变得流行，因为它为小型机器人提供了一种具有成本效益和轻量级的解决方案。与机械式 LiDAR 相比，固态 LiDAR 传感器具有更高的更新频率和角分辨率，但视野 (FoV) 更小，这对于现有的 LiDAR SLAM 算法非常具有挑战性。因此，有必要为这种新的传感设备提供一种更稳健、计算效率更高的 SLAM 方法。为此，我们提出了一种用于固态激光雷达传感器的新 SLAM 框架，该框架涉及特征提取、里程估计和概率图构建。所提出的方法在仓库机器人和手持设备上进行了评估。在实验中，我们使用英特尔 L515 固态激光雷达展示了我们方法的准确性和效率。结果表明，我们的方法能够提供精确的定位和高质量的映射。我们在 https://github.com/wh200720041/SSL_SLAM 上公开了源代码。

 6.Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

  IEEE ROBOTICS AND AUTOMATION LETTERS--2021

因子图融合Lidar、IMU、视觉。提出了一种从激光雷达点云中提取 3D 线和平面图元的新方法。

激光点云-->去畸变-->分割聚类（移除小聚类）-->计算局部曲率分类为角点和平面点-->跟踪平面和线（特征在其预测位置的局部附近被跟踪）（为了辅助跟踪，使用最大点到模型距离在预测的特征位置周围分割）

        We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches.

        我们提出了一种用于移动平台的高效多传感器里程计系统，该系统在单个集成因子图中联合优化视觉、激光雷达和惯性信息。这使用固定延迟平滑以全帧速率实时运行。为了执行这种紧密集成，提出了一种从激光雷达点云中提取 3D 线和平面图元的新方法。这种方法通过将基元视为地标并在多次扫描中跟踪它们，克服了典型帧到帧跟踪方法的次优性。使用激光雷达和相机帧的微妙被动同步，可以实现激光雷达功能与标准视觉功能和 IMU 的真正集成。 3D 特征的轻量级公式允许在单个 CPU 上实时执行。我们提出的系统已经在各种平台和场景上进行了测试，包括使用腿式机器人进行地下勘探和使用动态移动的手持设备进行户外扫描，总持续时间为 96 分钟，行驶距离为 2.4 公里。在这些测试序列中，仅使用一个外部感知传感器会由于几何约束不足（影响激光雷达）或由于剧烈的照明变化（影响视觉）导致无纹理区域而导致故障。在这些条件下，我们的因子图自然会使用每个传感器模式中可用的最佳信息，而无需任何硬开关。

7.An Intensity-Augmented LiDAR-Inertial SLAM for Solid-State LiDARs in Degenerated Environments

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT--2022

针对固态激光雷达的室内场景下的的退化问题

两个创新点：

1.基于几何和强度的特征提取（处理退化的核心）；

2.分别针对平面点和边缘点设计了两个多重加权函数（为了充分利用特征信息）；

3.图像处理方法的地图处理模块。

        With development of light detection and ranging (LiDAR) technology, solid-state LiDARs receive a lot of attention for their high reliability, low cost, and lightweight. However, compared with traditional rotating LiDARs, these solid-state LiDARs pose new challenges on simultaneous localization and mapping (SLAM) due to their small field of view (FoV) in horizontal direction and irregular scanning pattern, which arises the issue of degeneracy in indoor environments. To this end, we propose an accurate, robust, and real-time LiDAR-inertial SLAM method for solid-state LiDARs. First, a novel feature extraction based on geometry and intensity is proposed, which is the core of handling with degeneracy. To make full use of extracted features, two multi-weighting functions are designed for planar and edge points respectively in the process of pose optimization. Lastly, a map management module using an image processing method is developed not only to keep time efficiency and space efficiency but also to reduce edge intensity outliers in line map. Qualitative and quantitative evaluations on public and recorded datasets show that the proposed method exhibits similar and even better accuracy with state-of-the-art SLAM methods in well-constrained scenarios, while only the proposed method can survive in the robustness test toward degenerated indoor lab environment.

        随着光探测与测距 (LiDAR) 技术的发展，固态 LiDAR 因其高可靠性、低成本和轻量化而备受关注。然而，与传统的旋转式激光雷达相比，这些固态激光雷达由于水平方向的视场（FoV）小和不规则的扫描模式，对同时定位和建图（SLAM）提出了新的挑战，从而产生了室内环境退化的问题。为此，我们提出了一种用于固态 LiDAR 的准确、稳健和实时的 LiDAR 惯性 SLAM 方法。首先，提出了一种基于几何和强度的新特征提取，这是处理退化的核心。为了充分利用提取的特征，在位姿优化过程中分别针对平面点和边缘点设计了两个多重加权函数。最后，开发了一种使用图像处理方法的地图管理模块，不仅可以保持时间效率和空间效率，还可以减少线图中的边缘强度异常值。对公共和记录数据集的定性和定量评估表明，所提出的方法在受约束的场景中表现出与最先进的 SLAM 方法相似甚至更好的准确性，而只有所提出的方法才能在退化室内的鲁棒性测试中存活下来实验室环境。

8.Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial odometry Using Parallel Sparse Incremental Voxels

 IEEE ROBOTICS AND AUTOMATION LETTERS--2022

使用增量体素（iVox）作为空间数据结构，可并行K-NN查询。

        This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000-2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.

        本文介绍了一种基于增量体素的激光雷达惯性里程计 (LIO) 方法，用于快速跟踪旋转和固态激光雷达扫描。为了实现高跟踪速度，我们既不使用复杂的基于树的结构来划分空间点云，也不使用严格的 k 最近邻（k-NN）查询来计算点匹配。相反，我们使用增量体素（iVox）作为我们的点云空间数据结构，它是对传统体素的修改，并支持增量插入和并行近似 k-NN 查询。我们提出线性 iVox 和 PHC（伪希尔伯特曲线）iVox 作为我们算法中的两个替代基础结构。实验表明，iVox 在固态激光雷达中的每次扫描速度达到 1000-2000 赫兹，对于仅使用现代 CPU 的 32 线旋转激光雷达，iVox 的速度超过 200 赫兹，同时仍保持相同的精度水平。

9.RINet: Efficient 3D Lidar-Based Place Recognition Using Rotation Invariant Neural Network

IEEE ROBOTICS AND AUTOMATION LETTERS--2022

引入旋转不变的神经网络来进行位置识别（结合语义和几何信息）

        LiDAR-based place recognition (LPR) is one of the basic capabilities of robots, which can retrieve scenes from maps and identify previously visited locations based on 3D point clouds. As robots often pass the same place from different views, LPR methods are supposed to be robust to rotation, which is lacking in most current learning-based approaches. In this letter, we propose a rotation invariant neural network structure that can detect reverse loop closures even training data is all in the same direction. Specifically, we design a novel rotation equivariant global descriptor, which combines semantic and geometric features to improve description ability. Then a rotation invariant siamese neural network is implemented to predict the similarity of descriptor pairs. Our network is lightweight and can operate more than 8000 FPS on an i7-9700 CPU. Exhaustive evaluations and robustness tests on the KITTI, KITTI-360, and NCLT datasets show that our approach can work stably in various scenarios and achieve state-of-the-art performance.

        基于激光雷达的地点识别（LPR）是机器人的基本能力之一，它可以从地图中检索场景并基于 3D 点云识别之前访问过的位置。由于机器人经常从不同的视角经过同一个地方，因此 LPR 方法应该对旋转具有鲁棒性，这是当前大多数基于学习的方法所缺乏的。在这封信中，我们提出了一种旋转不变的神经网络结构，即使训练数据都在同一个方向上，它也可以检测到反向闭环。具体来说，我们设计了一种新颖的旋转等变全局描述符，它结合了语义和几何特征来提高描述能力。然后实现一个旋转不变的孪生神经网络来预测描述符对的相似性。我们的网络是轻量级的，可以在 i7-9700 CPU 上运行超过 8000 FPS。对 KITTI、KITTI-360 和 NCLT 数据集的详尽评估和稳健性测试表明，我们的方法可以在各种场景中稳定工作并实现最先进的性能。

10.Direct LiDAR Odometry: Fast Localization With Dense Point Clouds

IEEE ROBOTICS AND AUTOMATION LETTERS--2022

关键帧策略来使用密集、最少预处理的点云实现轻量化快速、准确的里程计估计

        Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To this end, this letter presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms. Our Direct LiDAR Odometry (DLO) method includes several key algorithmic innovations which prioritize computational efficiency and enables the use of dense, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This is achieved through a novel keyframing system which efficiently manages historical map information, in addition to a custom iterative closest point solver for fast point cloud registration with data structure recycling. Our method is more accurate with lower computational overhead than the current state-of-the-art and has been extensively evaluated in multiple perceptually-challenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge.

        感知挑战性环境中的现场机器人技术需要快速准确的状态估计，但现代 LiDAR 传感器很快就压倒了当前的里程计算法。为此，这封信提出了一种轻量级的前端 LiDAR 里程计解决方案，为计算受限的机器人平台提供一致且准确的定位。我们的直接 LiDAR 里程计 (DLO) 方法包括几个关键的算法创新，这些创新优先考虑计算效率，并能够使用密集的、最少预处理的点云来实时提供准确的姿态估计。这是通过一个新的关键帧系统来实现的，该系统有效地管理历史地图信息，此外还有一个用于快速点云注册和数据结构回收的自定义迭代最近点求解器。我们的方法比目前最先进的方法更准确，计算开销更低，并且作为 NASA JPL Team CoSTAR 对 DARPA Subterranean 的研发工作的一部分，已在空中和腿式机器人的多个感知挑战性环境中进行了广泛评估挑战。

11.EKF-LOAM: An Adaptive Fusion of LiDAR SLAM With Wheel Odometry and Inertial Data for Confined Spaces With Few Geometric Features

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING--2022

通过EKF融合车轮里程计和IMU到LeGO-LOAM中，解决真实机器人在几何特征很少的环境中所覆盖距离的低估问题。

        A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the EKF-LOAM , which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the EKF-LOAM uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the EKF-LOAM method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. Note to Practitioners —This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.

        精确的定位系统和正确表示环境的地图是多种机器人应用的基础。传统的 LiDAR SLAM 算法特别容易低估真实机器人在几何特征很少的环境中所覆盖的距离。常见的工业密闭空间，例如管道和走廊，具有长而均匀的结构，难以绘制地图。在本文中，我们提出了一种新方法，即 EKF-LOAM，它使用扩展卡尔曼滤波器将车轮里程计和 IMU（惯性测量单元）数据融合到 LeGO-LOAM 算法中。为此，EKF-LOAM 使用基于检测到的几何特征数量的简单且轻量级的自适应协方差矩阵。 EspeleoRobô 是一种用于检查受限场所的服务机器人，其模拟和实际实验表明，EKF-LOAM 方法减少了低估问题，与原来的 LeGO-LOAM 算法相比，改进了 50% 以上。从业者须知——本文的动机是移动地面机器人在受限和非结构化环境中的自主导航挑战。在这里，我们提出了一个数据融合框架，该框架使用常见的传感器（如激光雷达、车轮里程计和惯性设备）来提高没有 GPS 和指南针的机器人的同时定位和映射 (SLAM) 能力。这种方法不需要人工地标，也不需要理想的光线，并且在几何特征很少的场景中，提高了基于边缘和平面特征的 LiDAR SLAM 技术的性能。我们还为机器人在隧道绘图期间的自主导航提供了一个强大的控制器。在模拟和现实世界的密闭空间中进行的实验表明了我们方法的有效性。在未来的工作中，我们将结合其他传感器，例如相机，以改进 SLAM 过程。

12.OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition

 IEEE ROBOTICS AND AUTOMATION LETTERS--2022

提出了利用变压器网络的偏航角不变架构的神经网络，解决基于3D Lidar的位置识别问题

        Place recognition is an important capability for autonomously navigating vehicles operating in complex environments and under changing conditions. It is a key component for tasks such as loop closing in SLAM or global localization. In this letter, we address the problem of place recognition based on 3D LiDAR scans recorded by an autonomous vehicle. We propose a novel lightweight neural network exploiting the range image representation of LiDAR sensors to achieve fast execution with less than 2 ms per frame. We design a yaw-angle-invariant architecture exploiting a transformer network, which boosts the place recognition performance of our method. We evaluate our approach on the KITTI and Ford Campus datasets. The experimental results show that our method can effectively detect loop closures compared to the state-of-the-art methods and generalizes well across different environments. To evaluate long-term place recognition performance, we provide a novel dataset containing LiDAR sequences recorded by a mobile robot in repetitive places at different times.

        位置识别是在复杂环境和不断变化的条件下自动导航车辆的重要能力。它是 SLAM 中的闭环或全局定位等任务的关键组件。在这封信中，我们解决了基于自动驾驶汽车记录的 3D LiDAR 扫描的地点识别问题。我们提出了一种新颖的轻量级神经网络，利用激光雷达传感器的距离图像表示来实现每帧不到 2 毫秒的快速执行。我们设计了一个利用变压器网络的偏航角不变架构，这提高了我们方法的位置识别性能。我们在 KITTI 和 Ford Campus 数据集上评估我们的方法。实验结果表明，与最先进的方法相比，我们的方法可以有效地检测回环，并且可以很好地泛化不同的环境。为了评估长期位置识别性能，我们提供了一个新的数据集，其中包含移动机器人在不同时间重复位置记录的 LiDAR 序列。

13.MMDF: Multi-Modal Deep Feature Based Place Recognition of Mobile Robots With Applications on Cross-Scene Navigation

 IEEE ROBOTICS AND AUTOMATION LETTERS--2022

引入神经网络提取激光雷达和视觉特征并进行融合，从而解决跨场景的不稳定问题

        Although the navigation of robots in urban environments has achieved great performance, there is still a problem of insufficient robustness in cross-scene (ground, water surface) navigation applications. An intuitive idea is to introduce multi-modal complementary data to improve the robustness of the algorithms. Therefore, this paper presents an MMDF (multi-modal deep feature) based cross-scene place recognition framework, which consists of four kinds of modules: LiDAR module, image module, fusion module and NetVLAD module. 3D point clouds and images are input to the network firstly. The point cloud module uses PointNet to extract point cloud features. The image module uses a lightweight network to extract image features. The fusion module uses image semantic features to enhance point cloud features, and then the enhanced point cloud features are aggregated using NetVLAD to obtain the final enhanced descriptors. Extensive experiments on KITTI, Oxford RobotCar and USVlnland datasets demonstrate MMDF outperforms PointNetVLAD, NetVLAD and a camera-LiDAR fused descriptor.

        尽管机器人在城市环境中的导航取得了不错的成绩，但在跨场景（地面、水面）导航应用中仍然存在鲁棒性不足的问题。一个直观的想法是引入多模态互补数据来提高算法的鲁棒性。因此，本文提出了一种基于MMDF（multi-modal deep feature）的跨场景地点识别框架，该框架由四种模块组成：LiDAR模块、图像模块、融合模块和NetV​​LAD模块。 3D点云和图像首先输入到网络中。点云模块使用PointNet来提取点云特征。图像模块使用轻量级网络来提取图像特征。融合模块利用图像语义特征增强点云特征，然后利用NetVLAD对增强后的点云特征进行聚合，得到最终的增强描述符。在 KITTI、Oxford RobotCar 和 USVlnland 数据集上进行的大量实验表明，MMDF 优于 PointNetVLAD、NetVLAD 和相机-LiDAR 融合描述符。