大规模环境下基于图优化SLAM的图构建方法

分析总结了基于图优化同步定位和地图构建(SLAM)前端图构建过程的各种方法.对现有SLAM研究方法进行分类,指出基于Kalman滤波器、粒子滤波器、图优化方法的优缺点;重点介绍SLAM问题的3种图建模方法,即动态贝叶斯网络的图建模方法、基于因子图的建模方法、基于Markov随机场的建模方法;对图优化SLAM方法前端图构建的核心环节——帧间数据关联和环形闭合检测方法进行了分析;讨论了特征提取、特征匹配、运动估计、环形闭合检测等方面的最新研究成果.     

基于双目强约束的直接稀疏视觉里程计

为了提高双目直接稀疏里程计(Stereo Direct Sparse Odometry,Stereo DSO)的定位速度和精度,使得移动机器人可以更有效地执行任务,提出了一种基于双目强约束的直接稀疏视觉里程计系统.基于直接法的即时定位与地图构建(Simultaneous Localization and Mapping...     

基于可视点法剔除动态目标的激光-惯导SLAM

针对传统激光同时定位与建图在动态环境中位姿估计累计误差大、地图中存在动态目标错误点云的问题,本文提出了一种基于可视点法实时剔除动态目标的激光-惯导SLAM方法(DM-LIO)。该方法使用IMU测量值为基于可视点法的动态目标剔除模块提供先验位姿,并引入基于弯曲体素空间的点云聚类方法,以解决在低分辨率可视点法下动态点不能被完全捕捉的问题,从而实现了在算法前端剔除激光点云中的动态目标。本文通过自主搭建室内真机实验平台和使用公开数据集两种方式对算法性能进行评估。真机实验结果表明本文提出的DM-LIO能够对多个动态目标以及非先验动态目标进行实时剔除;在公开数据集Urbanloco上的测试结果表明,在高动态的环境下DM-LIO的绝对轨迹误差相较于LIO-SAM减少了60%以上,验证了该算法在高动态环境中具有良好的定位精度。     

基于RGB-D图像的室内机器人同时定位与地图构建

同时定位与地图构建（SLAM）是机器人在未知环境实现自主导航的关键技术，针对目前常用的RGB-D SLAM系统实时性差和精确度低的问题，提出一种新的RGB-D SLAM系统，以进一步提升实时性和精确度。首先，采用ORB算法检测图像特征点，并对提取的特征点采用基于四叉树的均匀化策略进行处理，并结合词袋模型（BoW）进行特征匹配。然后，在系统相机姿态初始值估计阶段，结合PnP和非线性优化方法为后端优化提供一个更接近最优值的初始值；在后端优化中，使用光束法平差（BA）对相机姿态初始值进行迭代优化，从而得到相机姿态的最优值。最后，根据相机姿态和每帧点云地图的对应关系，将所有的点云数据注册到同一个坐标系中，得到场景的稠密点云地图，并对点云地图利用八叉树进行递归式的压缩以得到一种用于机器人导航的三维地图。在TUM RGB-D数据集上，将构建的RGB-D SLAM同RGB-D SLAMv2、ORB-SLAM2系统进行了对比，实验结果表明所构建的RGB-D SLAM系统在实时性和精确度上的综合表现更优。     

Vision-Based SLAM: Stereo and Monocular Approaches

Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.     

抛光型SLAM系统灵敏度研究

以片式电子材料元器件作为无损检测分析对象,以提高激光扫描声学显微镜(SLAM)系统灵敏度为目标,详细分析了入射声波在抛光型分层介质中的传播规律,建立了抛光型样品自由界面动态波纹振幅数学模型。经综合分析获得了提高系统响应的方法,为进一步改进SLAM提供了理论依据。     

A review of visual inertial odometry from filtering and optimisation perspectives

Visual inertial odometry (VIO) is a technique to estimate the change of a mobile platform in position and orientation overtime using the measurements from on-board cameras and IMU sensor. Recently, VIO attracts significant attentions from large number of researchers and is gaining the popularity in various potential applications due to the miniaturisation in size and low cost in price of two sensing modularities. However, it is very challenging in both of technical development and engineering implementation when accuracy, real-time performance, robustness and operation scale are taken into consideration. This survey is to report the state of the art VIO techniques from the perspectives of filtering and optimisation-based approaches, which are two dominated approaches adopted in the research area. To do so, various representations of 3D rigid motion body are illustrated. Then filtering-based approaches are reviewed, and followed by optimisation-based approaches. The links between these two approaches will be clarified via a framework of the Bayesian Maximum A Posterior. Other features, such as observability and self calibration, will be discussed.     

Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Sound source localization is an important function in robot audition. Most existing works perform sound source localization using static microphone arrays. This work proposes a framework that simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot. First, an eigenstructure-based generalized cross-correlation method for estimating time delays between microphones under multi-source environments is described. Using the estimated time delays, a method to compute the farfield source directions as well as the speed of sound is proposed. In addition, the correctness of the sound speed estimate is utilized to eliminate spurious sources, which greatly enhances the robustness of sound source detection. The arrival angles of the detected sound sources are used as observations in a bearing-only simultaneous localization and mapping procedure. As the source signals are not persistent and there is no identification of the signal content, data association is unknown and it is solved using the FastSLAM algorithm. The experimental results demonstrate the effectiveness of the proposed method.     

A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.