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.

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.

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.

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.

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 survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Approach of simultaneous localization and mapping based on local maps for robot

An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.

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.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

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 navigation error.

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IPSO-Gmapping算法的SLAM系统研究

针对传统Gmapping算法因粒子耗散导致定位精度不准确的现象,改进粒子群算法(IPSO)结合Gmapping算法(IPSO-Gmapping)被提出。通过引入相似度测量参数和新的学习因子,IPSO算法中粒子的全局开发能力得到提升,同时避免了陷入“局部最优值”的现象。其次将IPSO算法应用于传统的Gmapping中,使得粒子向高似然区域移动,改善了粒子的分布状态,这也使得IPSO-Gmapping算法表现出了极好的性能。分别使用公共数据集和实际场景进行验证,总体的平移旋转误差大幅度降低。通过实验测试表明,所提出的IPSO-Gmapping算法使用更少的粒子在位姿估计准确性及建图精确性上优于传统的Gmapping算法。

视觉SLAM方法综述

实时定位与建图(SLAM)技术搭载特定传感器,使移动机器人在无任何环境先验条件下,在运动过程中自主建立环境模型来计算自身位姿,大幅提高其自主导航能力,以及对不同应用环境的适应性。视觉SLAM方法以相机作为外部传感器,通过采集周围环境信息来创建地图并实时估计机器人自身位姿。为此,介绍了具有代表性的经典视觉SLAM方法及与深度学习相结合的视觉SLAM方法,分析了视觉SLAM方法中采用的不同特征检测方法、后端优化、闭环检测,以及动态环境下视觉SLAM方法的应用,总结了视觉SLAM方法的问题,并探讨了视觉SLAM方法在未来的热点研究方向和发展前景。

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。

激光雷达/IMU/车辆运动学约束紧耦合SLAM算法

针对自动驾驶车辆在全球导航卫星系统(GNSS)信号不足场景下的定位需求,提出了一种激光雷达/惯性测量装置(IMU)/车辆运动学约束紧耦合的同时定位与地图构建(SLAM)算法。首先,基于IMU角速度、车辆后轴轮速和前轮转角构建车辆运动学约束,将车辆运动的位移和姿态信息解耦,构建位移和姿态约束以提高优化结果的准确性;然后,根据点云特征点数量和车辆转向角度引入自适应调整系数,实时调节车辆运动学约束的权重。最后,基于IMU角速度和车辆后轴轮速构建里程计模型,为后端紧耦合优化提供精准的初始值,避免陷入局部最优。不同道路场景下的测试结果表明,所提算法与LeGO_LOAM和LIO_SAM算法相比,平均平面定位精度分别提高了32%和29%,为自动驾驶车辆提供了一种GNSS信号不足情况下的短时高精度定位解决方案。

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。

退化环境中基于空间几何特征的激光SLAM方法

针对同步定位与地图构建(SLAM)在无全球定位系统(GPS)信号、缺乏环境特征纹理的退化环境中出现定位失败、建图重叠漂移和无法实时运行的问题,提出了一种基于空间几何特征的激光SLAM方法。算法前端设计了一种基于空间线面几何特征的特征点提取方式,依据点线面约束构建点云配准残差函数,采用高斯-牛顿法优化残差以实现点云配准。算法后端基于关键帧构建子图,通过子图间的map-to-map匹配来获得精确位姿,采用插值融合前后端位姿,实现了全局定位优化。仿真与实际退化环境中的实验结果表明:所提算法在20 m的里程计测试中位姿估算误差小于5%;退化环境中的建图效果优于Hector、Gmapping和Cartographer算法,地图更新平均速度提高近4倍。

复杂环境下基于自适应极线约束的AGV视觉SLAM算法

针对传统视觉同步定位与地图构建(SLAM)算法不能有效处理复杂环境中的动态及潜在动态目标而影响定位与建图性能的问题,提出一种基于Mask R-CNN神经网络以及ORB-SLAM3算法改进的视觉SLAM方法。针对动态目标,提出一种基于语义信息的运动一致性检验算法,使用自适应阈值的极线约束方法实现图像中动态特征点的精确剔除;针对潜在动态目标,提出一种改进的长期数据关联方法,通过增大关键帧选取密度及优化关键帧中的潜在动态目标信息,对算法的回环优化和地图融合过程进行改进,提高回环优化效果与地图复用性。在TUM数据集和真实场景中进行验证,实验结果表明与ORB-SLAM3算法相比,采用TUM数据集在低动态场景和高动态场景中的绝对轨迹均方根误差分别减小8.5%和65.6%;在真实场景下测试,所提算法的定位精度提高了62.5%。