两种基于激光雷达的SLAM算法最优参数分析

激光雷达是移动机器人同步定位与地图构建(SLAM)的重要模块.对目前主流的基于激光雷达的SLAM方法(Gmapping和Hector SLAM)进行研究,借助开源机器人操作系统(ROS),在自主研发的移动机器人平台上配备激光雷达,实现了不同参数配置下两种算法的地图构建.实验为参数最优配置指明了方向,且证明了Hector SLAM的整体构图精度高于Gmapping,但对参数配置要求较高.     

基于MIEKF的移动机器人同时定位与地图构建研究*

由于移动机器人处在未知并且不确定的环境中,主要采用基于概率的方法对同时定位与地图构建(SLAM)进行描述。本文建立了SLAM问题的概率表示模型,并对在解决SLAM问题中用最常用的扩展卡尔曼滤波(EKF)算法以及迭代扩展卡尔曼滤波(IEKF)算法进行描述。本文针对两种算法的缺陷和不足,将应用于跟踪领域的修正迭代扩展卡尔曼滤波算法(MIEKF)与SLAM思想结合,提出了一种新的基于MIEKF的SLAM算法。通过基于点特征的SLAM实验验证了该算法的有效性。     

激光即时定位与建图算法综述

激光即时定位与建图(SLAM)算法是一种在机器人导航和自主驾驶领域被广泛应用的技术;该技术可以利用激光雷达扫描环境并提取特征点,实现机器人的自主定位和地图构建;针对机器人激光SLAM技术进行研究,分析了各个激光SLAM算法的基本原理,并且对主流SLAM算法进行了现状总结;根据激光SLAM算法的特点以及原理不同,将激光SLAM算法分为：基于滤波器的算法、基于图优化的算法、基于配准的算法、基于学习的算法等;基于上述分类,详细介绍了每个算法的优缺点,并且分述了近两年的主要研究成果;针对移动机器人激光SLAM算法研究现状,对激光SLAM算法的未来发展进行了展望。     

Visual‐inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points

We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full six degrees of freedom pose of a robot while providing a structured map that can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the three‐dimensional (3D) location of curve landmarks from a stereo pair and how to compare the 3D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point‐based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state‐of‐the‐art SLAM algorithms.     

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments     

SLAM 问题中机器人定位误差分析与控制

移动机器人同步定位与建图问题 (Simultaneous localization and mapping, SLAM) 是机器人能否在未知环境中实现完全自主的关键问题之一. 其中, 机器人定位估计对于保持地图的一致性非常重要. 本文分析了 SLAM 问题中机器人定位误差的收敛特性. 分析表明随着机器人的运动,机器人定位误差总体上逐渐增大; 在完全未知环境中无法预测机器人定位误差的上限. 根据理论分析, 本文提出了一种控制机器人定位误差在单位距离上增长速度的算法. 该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度, 从而控制机器人定位误差的增长.     

An homogeneous space geometry for simultaneous localisation and mapping

Simultaneous Localisation and Mapping (SLAM) is the archetypal chicken and egg problem: Localisation of a robot with respect to a map requires an estimate of the map, while mapping an environment from data acquired by a robot requires an estimate of the robot localisation. The nonlinearity and co-dependence of the SLAM problem has made it an ongoing research problem for more than thirty years. The present paper details recent advances in understanding the SLAM problem, specifically the existence of an underlying geometry and symmetry structure that provides significant insight into the difficulties that have plagued many SLAM algorithms. To demonstrate the power of the geometric insight we derive a constant gain observer for the SLAM problem that; that does not depend on linearisation, has globally asymptotically stable error dynamics, is very robust, and operates in dynamic environments (estimating the landmark velocities as states in the observer).     

A SLAM based on auxiliary marginalised particle filter and differential evolution

FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.     

基于深度强化学习的室内视觉局部路径规划

传统的机器人局部路径规划方法多为已有先验地图的情况设计,导致其在与视觉(simultaneous localization and mapping, SLAM)结合的导航中效果不佳。为此传统的机器人局部路径规划方法多为已有先验地图的情况设计,导致其在与视觉SLAM结合的导航中效果不佳。为此,本文提出一种基于深度强化学习的视觉局部路径规划策略。首先,基于视觉同时定位与建图(SLAM)技术建立周围环境的栅格地图,并使用A*算法规划全局路径;其次,综合考虑避障、机器人行走效率、位姿跟踪等问题,构建基于深度强化学习的局部路径规划策略,设计以前进、左转、右转为基本元素的离散动作空间,以及基于彩色图、深度图、特征点图等视觉观测的状态空间,利用近端策略优化(proximal policy optimization, PPO)算法学习和探索最佳状态动作映射网络。Habitat仿真平台运行结果表明,所提出的局部路径规划策略能够在实时创建的地图上规划出一条最优或次优路径。相比于传统的局部路径规划算法,平均成功率提高了53.9%,位姿跟踪丢失率减小了66.5%,碰撞率减小了30.1%。     

An approach to robot SLAM based on incremental appearance learning with omnidirectional vision

Localisation and mapping with an omnidirectional camera becomes more difficult as the landmark appearances change dramatically in the omnidirectional image. With conventional techniques, it is difficult to match the features of the landmark with the template. We present a novel robot simultaneous localisation and mapping (SLAM) algorithm with an omnidirectional camera, which uses incremental landmark appearance learning to provide posterior probability distribution for estimating the robot pose under a particle filtering framework. The major contribution of our work is to represent the posterior estimation of the robot pose by incremental probabilistic principal component analysis, which can be naturally incorporated into the particle filtering algorithm for robot SLAM. Moreover, the innovative method of this article allows the adoption of the severe distorted landmark appearances viewed with omnidirectional camera for robot SLAM. The experimental results demonstrate that the localisation error is less than 1 cm in an indoor environment using five landmarks, and the location of the landmark appearances can be estimated within 5 pixels deviation from the ground truth in the omnidirectional image at a fairly fast speed.     

面向地图构建的移动机器人局部路径自主规划

移动机器人在未知场景中规划路径以自主完成定位与地图构建是机器人领域的一个重要研究课题.本文阐述了一种利用实时构建的信息熵地图动态生成机器人的局部探索路径,并综合转向约束和避障约束设计了一种基于模糊评价方法的方向选择策略跟踪生成的局部路径并进行环境构图.与现有方法相比,本文方法能够根据环境动态地生成平滑连续的局部探索路径,并能引导机器人进行障碍物躲避和完成自主构图.实验结果表明相较对比方法,本文方法的探索路程最短,观测覆盖度最高,同时整个自主构图过程所需的时间也更短.     

GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments

As a research hotspot in the field of robotics, Simultaneous localization and mapping (SLAM) has made great progress in recent years, but few SLAM algorithms take dynamic or movable targets in the scene into account. In this paper, a robust new RGB-D SLAM method with dynamic area detection towards dynamic environments named GMSK-SLAM is proposed. Most of the existing related papers use the method of directly eliminating the whole dynamic targets. Although rejecting dynamic objects can increase the accuracy of robot positioning to a certain extent, this type of algorithm will result in the reduction of the number of available feature points in the image. The lack of sufficient feature points will seriously affect the subsequent precision of positioning and mapping for feature-based SLAM. The proposed GMSK-SLAM method innovatively combines Grid-based Motion Statistics (GMS) feature points matching method with K-means cluster algorithm to distinguish dynamic areas from the images and retain static information from dynamic environments, which can effectively increase the number of reliable feature points and keep more environment features. This method can achieve a highly improvements on localization accuracy in dynamic environments. Finally, sufficient experiments were conducted on the public TUM RGB-D dataset. Compared with ORB-SLAM2 and the RGB-D SLAM, our system, respectively, got 97.3% and 90.2% improvements in dynamic environments localization evaluated by root-mean-square error. The empirical results show that the proposed algorithm can eliminate the influence of the dynamic objects effectively and achieve a comparable or better performance than state-of-the-art methods.

     

基于粒子滤波的无线传感器网络辅助同步定位与地图创建方法研究

提出了一种新颖的无线传感器网络（WSN）辅助的移动机器人同步定位与地图创建（SLAM）方法, 解决了传统SLAM 方法难以解决的求解问题空间维数高和多数据关联困难两大问题．为该WSN 辅助的SLAM 方法建立了模型,并进行了噪声分析;在此基础上,提出一种适用本方法的分布式粒子滤波数据融合算法．着重 分析了粒子初始化、预测、序贯重要性采样和重采样等关键步骤,并通过仿真实验分析验证了该方法的正确性和 高效率．实验结果表明,采用粒子滤波算法,并综合无线传感器网络进行辅助导航,可以极大地降低求解问题空 间维数,解决多数据关联错误问题,可以完全不依赖锚节点完成盲节点高精度定位;同时,还能够有效地提高移 动机器人定位与地图创建精度,特别是在不要求机器人路径闭合的情况下可以有效抑制惯性导航的误差累计．     

未知环境下移动机器人同步地图创建与定位研究进展

移动机器人同步地图创建与定位(SLAM)是移动机器人的核心研究课题.本文对SLAM的最新研究进展和关键技术进行了综述:并从地图创建模型、计算复杂度和算法鲁棒性等方面对现有方法进行了对比分析.最后总结分析了SLAM研究存在的难题,探讨了今后的发展方向.     

A new approach to solve SLAM challenges by relative map filter

In this paper we propose a new approach to solve some challenges in the simultaneous localization and mapping (SLAM) problem based on the relative map filter (RMF). This method assumes that the relative distances between the landmarks of relative map are estimated fully independently. This considerably reduces the computational complexity to average number of landmarks observed in each scan. To solve the ambiguity that may happen in finding the absolute locations of robot and landmarks, we have proposed two separate methods, the lowest position error (LPE) and minimum variance position estimator (MVPE). Another challenge in RMF is data association problem where we also propose an algorithm which works by using motion sensors without engaging in their cumulative error. To apply these methods, we switch successively between the absolute and relative positions of landmarks. Having a sufficient number of landmarks in the environment, our algorithm estimates the positions of robot and landmarks without using motion sensors and kinematics of robot. Motion sensors are only used for data association. The empirical studies on the proposed RMF-SLAM algorithm with the LPE or MVPE methods show a better accuracy in localization of robot and landmarks in comparison with the absolute map filter SLAM.     

VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments

In this paper, we present an efficient SLAM (Simultaneous Localization and Mapping) algorithm named VecSLAM, which localizes and builds a vector map for mobile robots in indoor environments. Compared to grid-mapping approaches, vector-based mapping algorithms require a relatively small amount of memory. Two essential operations for successful vector mapping are vector merging and loop closing. Merging methods used by traditional line segment-based mapping algorithms do not consider the sensor characteristics, which causes additional mapping error and makes it harder to close loops after navigation over a long distance. In addition, few line segment-based SLAM approaches contain loop closing methodology. We present a novel vector merging scheme based on a recursive least square estimation for robust mapping. An efficient loop closing method is also proposed, which effectively distributes the resultant mapping error throughout the loop to guarantee global map consistency. Simulation studies and experimental results show that VecSLAM is an efficient and robust online localization and mapping algorithm.     

基于势场路径规划的机器人同步定位与地图构建

针对特征点同时为障碍物的环境,提出一种基于势场路径规划的同步定位与地图构建算法.机器人在同步定位与地图构建的同时,利用势场原理确定机器人的运动控制律,再根据推算的控制律进行下一步的预测和状态估计.在基于势场的路径规划方法中,认定为障碍物的排斥势位的最小影响范围可调节.实验结果表明,利用所提出的算法,机器人可在特征点同时为障碍物的环境中进行同步定位与地图构建,并通过相关性能指标验证了该算法为一致性估计.     

Topological map-based approach for localization and mapping memory optimization

Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.     

移动机器人导航空间表示及SLAM问题研究

导航研究是移动机器人研究的承要领域之一。 空间表示则是移动机器人导航研究的基础性问题。围绕移动机器人导航空间表示,该文首先对目前广泛采用的空间分解表示,几何特征表示,拓扑地图表示等多种移动机器人导航空间表示方法进行详细的归纳和总结。通过对移动机器人导航空间各种表示疗法进行性能对比,指出各种空间表示方法的优点与不足。最后,对移动机器人导航研究中的同时定位与地图创建（SLAM）问题作了阐述,指出SLAM研究面临的问题,探讨了SLAM的未来研究方向。     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.