Review of Simultaneous Localization and Mapping Technology in the Agricultural Environment

Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed.

Research on simultaneous localization and mapping for AUV by an improved method:Variance reduction FastSLAM with simulated annealing

At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.

Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping

Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.

Mobile Robot Hierarchical Simultaneous Localization and Mapping Using Monocular Vision

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.

A novel method for mobile robot simultaneous localization and mapping

A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.

Rapid State Augmentation for Compressed EKF-Based Simultaneous Localization and Mapping

A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project.

Simultaneous Localization and Mapping System Based on Labels

In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter（ EKF） for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.

Underwater Simultaneous Localization and Mapping Based on Forward-looking Sonar

A method of underwater simultaneous localization and mapping （SLAM） based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved association method based on an ant colony algorithm was introduced to estimate the positions. In order to improve the precision of the positions, the extended Kalman filter （EKF） was adopted. The presented algorithm was tested in a tank, and the maximum estimation error of SLAM gained was 0.25 m. The tests verify that this method can maintain better association efficiency and reduce navigatioJ~ error.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Localization and mapping in urban area based on 3D point cloud of autonomous vehicles

In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.

基于深度学习的移动机器人语义SLAM方法研究

为了给移动机器人提供细节丰富的三维语义地图,支撑机器人的精准定位,本文提出一种结合RGB-D信息与深度学习结果的机器人语义同步定位与建图方法。改进了ORB-SLAM2算法的框架,提出一种可以构建稠密点云地图的视觉同步定位与建图系统;将深度学习的目标检测算法YOLO v5与视觉同步定位与建图系统融合,反映射为三维点云语义标签,结合点云分割完成数据关联和物体模型更新,并用八叉树的地图形式存储地图信息;基于移动机器人平台,在实验室环境下开展移动机器人三维语义同步定位与建图实验,实验结果验证了本文语义同步定位与建图算法的语义信息映射、点云分割与语义信息匹配以及三维语义地图构建的有效性。

基于激光SLAM多地形机器人的设计

为解决传统轮式机器人在复杂地形中受限与腿式机器人控制策略复杂的问题,提出一种多地形机器人。结合激光即时定位与地图构建(SLAM)方法和自适应式轮腿机构,将树莓派作为运算单元,搭载(ROS)机器人操作系统,应用激光SLAM技术实现环境地图构建和机器人导航,同时结合深度模型和摄像头完成图像任务。自适应式轮腿机械结构使机器人能够根据环境需求自动切换为轮式或腿式行进模式。底层控制器采用STM32F407,机器人通过PID算法能实现精准的移动和机械臂作业。结果表明:该多地形机器人控制方法简单高效,在坡地、草地、坑地、台阶障碍物等复杂地形中展现了灵活移动的能力,最大翻越障碍高度可达250 mm,爬坡角度可达45°,在稳定性和适应性方面具有显著优势。

多传感器融合SLAM研究综述

如今,移动机器人技术的发展使得同步定位与建图(SLAM)技术越来越受到学者的关注。在未知环境下,使移动机器人能够自主完成建图或者探索,是SLAM最基本的要求。在过去的十年,单传感器为机器人的建图和探索提供了良好的效果,而多传感器融合SLAM则以其强鲁棒、高精度的技术特性,为提升移动机器人建图的精度和速度提供了更高的可能性,成为了SLAM发展的主要研究方向。文中总结了现今多传感器融合SLAM的方案,首先对单传感器方案进行了比较;然后对多传感器融合技术的方案进行了对比;最后,分析了多传感器融合SLAM的难点与解决方案,并对多传感器融合SLAM的未来与发展进行了探讨。

面向港口环境精细感知的无人船多传感器融合SLAM系统

针对港口环境高精度感知需求,综合考虑影响同时定位与建图(SLAM)精度的港口环境因素,提出基于多传感器融合的激光SLAM环境感知方案。通过分析多种传感器对港口SLAM环境感知的影响,引入惯性测量传感器弥补激光SLAM输出频率低和剧烈运动位姿估计不准确等缺陷,采用卫星定位系统信息进行高程数据约束,处理船舶运动特性导致的垂荡累计漂移。从应用需求出发,对传感器进行选型和布置优化,搭建基于无人船的港口环境多传感器融合SLAM系统。结果表明,提出的港口环境高精度点云地图获取方案能在典型港口场景下准确实时建图,为水面精细SLAM提供技术支持。

动态场景下基于实例分割与光流的语义SLAM建图

视觉同步定位与建图技术常用于室内智能机器人的导航,但是其位姿是以静态环境为前提进行估计的。为了提升视觉即时定位与建图(Simultaneous Localization And Mapping,SLAM)在动态场景中的定位与建图的鲁棒性和实时性,在原ORB-SLAM2基础上新增动态区域检测线程和语义点云线程。动态区域检测线程由实例分割网络和光流估计网络组成,实例分割赋予动态场景语义信息的同时生成先验性动态物体的掩膜。为了解决实例分割网络的欠分割问题,采用轻量级光流估计网络辅助检测动态区域,生成准确性更高的动态区域掩膜。将生成的动态区域掩膜传入到跟踪线程中进行实时剔除动态区域特征点,然后使用地图中剩余的静态特征点进行相机的位姿估计并建立语义点云地图。在公开TUM数据集上的实验结果表明,改进后的SLAM系统在保证实时性的前提下,提升了其在动态场景中的定位与建图的鲁棒性。

基于语义分割的视觉SLAM算法研究

目前大多数的视觉SLAM算法基于静态环境的假设,环境中的动态物体容易引起位姿估计的不准确。提出一种针对动态环境的改进算法。基于DS-SLAM方案进行改进,首先采用自适应阈值提取ORB特征点并通过改进四叉树算法将特征点均匀化;之后采用稀疏光流法跟踪角点的运动,同时结合Segment语义分割线程的结果分割动态物体;最后采用几何约束滤除动态点,保留高质量的特征点进行位姿估计,完成定位和建图功能。利用TUM数据集进行精度评测,相比于DS-SLAM算法,改进算法的实时性提升了9.02%。动态环境中相机位姿误差缩小了38.94%。通过提高特征点的质量,结合光流法和语义分割的优势,提升了机器人系统的定位精度和实时性。

SLAM技术及其在矿山无人驾驶领域的研究现状与发展趋势

无人驾驶是矿山智能化关键技术之一,其中即时定位与地图构建(SLAM)技术是实现无人驾驶的关键环节。为推动SLAM技术在矿山无人驾驶领域的发展,对SLAM技术原理、成熟地面SLAM方案、现阶段矿山SLAM研究现状、未来矿山SLAM发展趋势进行了探讨。根据SLAM技术所使用的传感器,从视觉、激光及多传感器融合3个方面分析了各自的技术原理及相应框架,指出视觉和激光SLAM技术通过单一相机或激光雷达实现,存在易受环境干扰、无法适应复杂环境等缺点,多传感器融合SLAM是目前最佳的解决方法。探究了目前矿山SLAM技术的研究现状,分析了视觉、激光、多传感器融合3种SLAM技术在井工煤矿、露天矿山的适用性与研究价值,指出多传感器融合SLAM是井工煤矿领域的最佳方案,SLAM技术在露天矿山领域研究价值不高。基于现阶段井下SLAM技术存在的难点(随时间及活动范围积累误差、各类场景引起的不良影响、各类传感器无法满足高精度SLAM算法的硬件要求),提出矿山无人驾驶领域SLAM技术未来应向多传感器融合、固态化、智能化方向发展。

SLAM精度的向量加权平均自适应调节研究

针对粒子滤波算法需要大量粒子以提高精度以及重采样导致的粒子多样性缺失的问题,提出一种自调INFO(向量加权平均算法)优化的粒子重组粒子滤波算法。首先,通过向量不同的加权平均规则,使得最优粒子引导粒子集向期望区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于阈值时,自适应调整迭代次数,实时监测粒子密度,根据此指标引入次优粒子的作用自适应调整粒子集分布;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较标准算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

基于多策略人工蜂鸟优化PF的SLAM研究

针对粒子滤波算法(PF)重采样导致粒子贫乏及需增加粒子数以提高估计精度的问题,提出一种基于多策略人工蜂鸟算法优化的粒子重组粒子滤波算法。首先,引入中垂线算法提高人工蜂鸟算法收敛速度,通过其智能觅食机制,使得最优粒子引导粒子集向高似然区域移动,以此提高估计精度;其次,实时计算最优粒子附近的粒子密度,当密度大于设置的区域搜索阈值时引入Levy飞行策略以扩大搜索空间,当其大于最大密度值时,自适应调整迭代次数;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。通过仿真实验检验改进算法在SLAM中的性能,结果表明该算法较其他3种算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

Dense Mapping From an Accurate Tracking SLAM

In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.