6D_SLAM_Tutorial_2007
SLAM介绍以及浅析ppt课件
感知信息进行匹配和联合，存在局部数据之间的关联问题，也存在局部数据 与全局数据的关联与匹配问题) (4) 自定位；
(移动机器人的定位按照有无环境地图可以分为基于地图的定位和无地图的 定位)
(5) 探索规划 (主要目的是提高地图创建的效率，使机器人在较短的时间内感知范围覆
·环境特征不够明显时； ·传感器信息比较少，难以从一次感知信息中获得环境特征。
不确定信息的描述和处理方法
在未知环境中，环境信息的不确定性尤为明显。研究人员已经提出了 多种用来处理不确定性的度量方法，如模糊度量、概率度量、信任度量、 可能性度量、证据理论度量等等。目前在SLAM中使用较多的主要是模糊 度量和概率度量的方法。
图3：路标C被观测到 （在新的位置，路标C被观测到，路标C的相对于A的位置也是一个估计值（更大的圈）
图4：路标B被观测到
图5：机器人返回到初始位置 （此时机器人的位置相对于没有移动前更加不确定，一个超大的椭圆表示了其可能的真实位置值范围）
图6：对A点的重新测量 （通过对A的重新测量，图4中的超大椭圆值被大大的缩小了，其位置真值落入了一个比较小的范围内）
说明：机器人在定位误差随着机器人的移动而增加。 说明：机器人在定位误差随着机器人的移动而增加，但是由于有了路标的纠正，其误差相对就小了很多。
说明：通过2次对路标的测定，其定位误差已经大大减少 说明：经过3次误差校正，其定位精度已经很好了，但是随着路标位置的丢失，其定位误差又一次的扩大了。
基于霍夫曼方法的SLAM的基本过程为：
①在一个未知的室内结构化环境中提出了基于栅格表示的局部模型与基于几何信息表
示的全局模型相结合的环境建模方式。环境特征的提取采用了Hough transform与Clustering 相结合的方法。感知数据的融合采用了扩展卡尔曼滤波方式。
slam实现方法
slam实现方法SLAM实现方法什么是SLAMSLAM，即Simultaneous Localization and Mapping，即同时定位与地图构建。
它是一种同时进行自主定位和地图建立的技术，通常用于无人机、自动驾驶和机器人等领域。
相关方法介绍基于视觉的SLAM方法基于视觉的SLAM方法主要利用摄像头获取环境信息，并通过图像处理和计算机视觉算法来实现同时定位和地图构建。
常用的方法包括：- 特征点法：通过提取图像中的特征点，利用这些特征点之间的匹配关系来计算相机的运动和地图的构建。
- 直接法：通过建立图像亮度的灰度残差模型，直接估计相机的运动和地图的构建。
- 深度学习法：利用深度学习的方法，通过训练神经网络来实现相机的定位和地图的构建。
基于激光的SLAM方法基于激光的SLAM方法主要利用激光雷达获取环境信息，并通过激光数据处理和SLAM算法来实现同时定位和地图构建。
常用的方法包括：- 自适应模型法：通过分析激光数据的反射特性，自适应地建立地图模型，同时进行定位。
- 点云拼接法：通过将多帧激光数据进行拼接，建立点云模型，同时进行定位。
- 分段匹配法：将激光数据进行分段匹配，利用匹配关系来计算相机的运动和地图的构建。
基于惯性传感器的SLAM方法基于惯性传感器的SLAM方法主要利用加速度计和陀螺仪等传感器来获取相机的运动信息，并通过滤波和融合算法来实现同时定位和地图构建。
常用的方法包括： - 扩展卡尔曼滤波法：通过预测和更新步骤，利用卡尔曼滤波算法来估计相机的位姿和地图的构建。
- 粒子滤波法：通过随机采样的方法，利用粒子滤波算法来估计相机的位姿和地图的构建。
- 单纯惯导法：通过积分惯性传感器的数据，估计相机的位姿变化，实现定位和地图构建。
结论SLAM是一种重要的技术，在无人机、自动驾驶和机器人等领域具有广泛应用。
基于视觉、激光和惯性传感器的SLAM方法都有各自的特点和适用场景，开发者可以根据具体应用需求选择合适的方法来实现SLAM。
slam回环检测方法总结
slam回环检测方法总结SLAM（Simultaneous Localization and Mapping）回环检测方法总结SLAM（同时定位与地图构建）是一种用于移动机器人或自主驾驶车辆在未知环境中进行自主导航的方法。
回环检测是SLAM中的一个关键环节，用于解决机器人在导航过程中产生位置估计累积误差的问题。
回环检测的核心目标是通过识别来自同一环境的不同视角观测数据，并纠正由于累积误差导致的位置估计偏差。
下面将介绍几种常见的SLAM回环检测方法：1. 特征匹配方法：特征匹配方法是最常见的回环检测方法之一。
它通过提取和匹配环境中的特征点，如角点或边缘点，来寻找不同视角之间的对应关系。
当两个观测数据中的特征点匹配数量达到一定阈值时，可以认为发现了一个回环。
特征匹配方法简单直观，但在存在大量重复纹理或视角变化较大的环境中可能存在较大的误匹配问题。
2. 视觉词袋方法:视觉词袋方法是一种基于图像内容描述的回环检测方法。
它通过提取图像中的局部特征，并将其表示为视觉词袋（visual bag of words）。
通过计算不同视角图像之间的视觉词袋之间的相似度，可以判断是否存在回环。
视觉词袋方法通常能够抵抗一定程度的光照变化和视角变化，但对于大规模环境的识别还存在一定的挑战。
3. 惯性测量单元（IMU）融合方法：IMU融合方法是一种结合了惯性测量单元（加速度计和陀螺仪）和视觉传感器的回环检测方法。
通过利用IMU的姿态信息，可以辅助回环检测，并提高位置估计的精度。
IMU融合方法通常能够有效解决视觉传感器在动态环境中的性能下降问题，但对于静止环境或缺乏IMU数据的情况下，性能可能下降。
4. 深度学习方法：近年来，深度学习方法在回环检测领域取得了显著的进展。
通过训练神经网络来学习图像的语义信息，可以实现更准确和鲁棒的回环检测。
深度学习方法通常需要大量的标注数据进行训练，并且对计算资源有一定的要求，但在回环检测任务中能够取得较好的性能。
SLAM经典入门教程
SLAM经典入门教程SLAM（Simultaneous Localization and Mapping）是指机器人或移动设备在未知环境中同时实现自身定位和环境地图构建的技术。
SLAM技术广泛应用于自动驾驶、无人机、智能家居等领域。
下面是一个经典的SLAM入门教程，帮助初学者了解SLAM的基本概念和实现方法。
一、SLAM的基本概念1. 定位（Localization）：机器人或移动设备在地图中确定自身位置的过程。
2. 地图构建（Mapping）：机器人或移动设备在移动过程中构建环境地图的过程。
3. 同步（Simultaneous）：指机器人或移动设备在进行定位和地图构建时同时进行，相互依赖、相互影响。
4. 自身定位误差（Self-localization error）：机器人或移动设备定位的准确性，影响其整体性能。
5. 地图构建误差（Mapping error）：机器人或移动设备构建环境地图的准确性，影响其对环境的理解能力。
二、SLAM的实现方法1.基于视觉的SLAM：利用摄像头或激光传感器获取环境信息，通过图像处理、特征提取、匹配等算法实现定位和地图构建。
2.基于激光雷达的SLAM：利用激光传感器扫描环境，通过计算得出物体的距离和位置，从而实现定位和地图构建。
3.基于惯性测量单元（IMU）的SLAM：利用加速度计、陀螺仪等传感器获取机器人的加速度和角速度信息，通过积分计算位姿，实现定位和地图构建。
4.基于里程计的SLAM：利用机器人的里程计测量轮子转动的距离，通过计算位姿变化来实现定位和地图构建。
三、经典SLAM算法1. EKF-SLAM（Extended Kalman Filter SLAM）：基于扩展卡尔曼滤波器的SLAM算法，利用状态估计和协方差矩阵来实现定位和地图构建。
2. FastSLAM：基于粒子滤波器的SLAM算法，将地图分解为多个粒子，每个粒子都有自己的状态和权重，通过多次重采样来更新地图。
直接法slam工作原理
直接法slam工作原理
《直接法SLAM工作原理》
SLAM（Simultaneous Localization and Mapping）是一种在未知环境中同时进行定位和地图构建的技术，它在无人驾驶、机器人导航等领域有着广泛的应用。
直接法SLAM是SLAM中的一种方法，它通过直接使用图像数据进行定位和地图构建，相比于传统的特征法SLAM，直接法SLAM能够更有效地利用图像信息。
直接法SLAM的工作原理主要包括以下几个步骤：
1. 视觉里程计（Visual Odometry）：通过连续的图像帧，使用特定的优化算法，计算出机器人在运动过程中的相对位姿变化。
这一步骤可以使用光流、特征点跟踪等方法来实现。
2. 稠密地图构建（Dense Map Construction）：利用机器人移动时获取的图像数据，将这些图像数据进行融合和优化，最终得到一个稠密的地图。
在这一步骤中，通常会使用优化算法，例如光束法束调整（Bundle Adjustment）来提高地图的精度。
3. 位姿估计（Pose Estimation）：通过已知的地图和机器人获取的图像数据，利用优化算法估计机器人在地图中的绝对位姿，这一步骤也是SLAM中的一个重要环节。
直接法SLAM的优点在于它可以直接利用图像数据进行定位和地图构建，不需要提取特征点等中间步骤，因此可以更好地利用图像信息，提高定位和地图构建的精度。
但同时，直接法SLAM也面临着光照变化、遮挡等挑战，需要更复杂的图像处理算法来应对这些问题。
总的来说，直接法SLAM通过直接使用图像数据进行定位和地图构建，为SLAM技术的发展提供了一种新的思路和方法，它在未来的无人驾驶、机器人导航等领域有着广阔的应用前景。
slam本科毕设题目
slam本科毕设题目
SLAM（Simultaneous Localization and Mapping）是一个复杂的研究领域，涉及机器人技术、计算机视觉和传感器融合等多个学科。
因此，对于本科生来说，完成一个完整的SLAM项目作为毕业设计题目是一个很大的挑战。
不过，我可以为你提供一些可能的题目和思路，供你参考：
1. 基于RGB-D相机的SLAM系统实现
在这个题目中，你可以使用微软的Kinect或Intel的RealSense等RGB-D相机作为传感器，通过实现一个SLAM系统来处理3D场景中的定位和地图构建问题。
你可以选择使用开源的SLAM框架，如ORB-SLAM或LOAM等，并根据需要进行修改和优化。
2. 基于激光雷达的SLAM系统实现
激光雷达是一种常用的传感器，可以用于实现精确的SLAM系统。
在这个题目中，你可以选择使用开源的激光雷达数据集，如KITTI或Velodyne HDL32E等，并实现一个SLAM系统来处理激光雷达数据。
你可以选择使用开源的SLAM框架，如Cartographer或LeGO-LOAM 等，并根据需要进行修改和优化。
3. 基于IMU和轮速传感器的SLAM系统实现
在这个题目中，你可以使用IMU（Inertial Measurement Unit）和轮速传感器来获取机器人的运动信息，并实现一个SLAM系统来处理机器人在动态环境中的定位和地图构建问题。
你可以选择使用开源的
SLAM框架，如Cartographer或OKVIS等，并根据需要进行修改和优化。
slam 平面检测原理
slam 平面检测原理
SLAM平面检测原理主要基于几何约束关系和姿态解算。
在两帧之间寻找不共线的三个点，构成一个平面，然后寻找与该平面距离最近的点，这些点的集合构成一个平面点集合。
根据这些平面点集合，可以进行优化，如去除异常值、剔除动态物体等，以提高SLAM的精度和鲁棒性。
在具体实现中，可以通过RANSAC算法（随机采样一致性）进行外点处理，以解决部分异常点对于算法的影响。
当环境中存在动态物体时，可以通过检测运动的物体、剔除动态区域的特征点，或降低在优化位姿的权重，减少对视觉定位的影响。
融合激光SLAM与视觉SLAM技术在矿山地下环境勘查与治理中的应用研究
融合激光SLAM与视觉SLAM技术在矿山地下环境勘查与治理中的应用研究目录一、内容概览 (2)1. 研究背景和意义 (3)1.1 矿山地下环境的重要性 (4)1.2 激光SLAM与视觉SLAM技术的融合应用前景 (5)2. 研究现状和发展趋势 (7)2.1 国内外研究现状 (8)2.2 技术发展趋势与挑战 (9)二、激光SLAM技术概述及其在矿山地下环境中的应用 (10)1. 激光SLAM技术原理及特点 (12)1.1 激光SLAM技术的基本原理 (13)1.2 激光SLAM技术的特点分析 (14)2. 激光SLAM技术在矿山地下环境中的应用现状 (15)2.1 地下环境建模与测量应用 (16)2.2 地下空间定位与导航应用 (18)三、视觉SLAM技术概述及其在矿山地下环境中的应用 (19)1. 视觉SLAM技术原理及特点 (21)1.1 视觉SLAM技术的基本原理 (22)1.2 视觉SLAM技术的特点分析 (24)2. 视觉SLAM技术在矿山地下环境中的应用现状与挑战 (25)2.1 应用现状分析 (27)2.2 技术挑战与问题探讨 (28)四、激光SLAM与视觉SLAM技术的融合研究及其应用前景分析 (29)1. 技术融合的原理与方法探讨 (31)1.1 数据融合的基本原理与方法研究 (32)1.2 激光与视觉信息融合的技术路径探讨 (33)2. 融合技术在矿山地下环境勘查与治理中的应用案例分析 (35)一、内容概览本研究旨在深入探讨融合激光SLAM与视觉SLAM技术在矿山地下环境勘查与治理中的实际应用。
随着工业化的快速发展，矿山开采日益频繁，随之而来的是矿山地下环境的复杂性和安全隐患日益突出。
为了高效、准确地掌握矿山地下环境状况，并进行科学有效的治理，我们提出了结合激光SLAM和视觉SLAM技术的解决方案。
激光SLAM技术以其高精度、高动态范围以及能够实时构建环境三维地图的能力，在室内定位、导航、地图创建等方面展现出了显著优势。
SLAM Tutorial Part I
Simultaneous Localisation and Mapping(SLAM): Part I The Essential AlgorithmsHugh Durrant-Whyte,Fellow,IEEE,and Tim BaileyAbstract—This tutorial provides an introduction to Simul-taneous Localisation and Mapping(SLAM)and the exten-sive research on SLAM that has been undertaken over the past decade.SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it’s own location.The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods.Part I of this tutorial(this paper),de-scribes the probabilistic form of the SLAM problem,essen-tial solution methods and significant implementations.Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.I.IntroductionThe Simultaneous Localisation and Mapping(SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its lo-cation within this map.A solution to the SLAM problem has been seen as a‘holy grail’for the mobile robotics com-munity as it would provide the means to make a robot truly autonomous.The‘solution’of the SLAM problem has been one of the notable successes of the robotics community over the past decade.SLAM has been formulated and solved as a theoretical problem in a number of different forms.SLAM has also been implemented in a number of different domains from indoor robots,to outdoor,underwater and airborne systems.At a theoretical and conceptual level,SLAM can now be considered a solved problem.However,substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm.This two-part tutorial and survey of SLAM aims to pro-vide a broad introduction to this rapidly growingfield. Part I(this paper)begins by providing a brief history of early developments in SLAM.Section III introduces the structure the SLAM problem in now standard Bayesian form,and explains the evolution of the SLAM process. Section IV describes the two key computational solutions to the SLAM problem;through the use of the extended Kalmanfilter(EKF-SLAM)and through the use of Rao-Blackwellised particlefilters(FastSLAM).Other recent so-lutions to the SLAM problem are discussed in Part II of Hugh Durrant-Whyte,and Tim Bailey are with the Australian Centre for Field Robotics(ACFR)J04,The University of Sydney,Sydney NSW2006,Australia, hugh@.au,tbailey@.au this tutorial.Section V describes a number of important real-world implementations of SLAM and also highlights implementations where the sensor data and software are freely down-loadable for other researchers to study.Part II of this tutorial describes major issues in computation, convergence and data association in SLAM.These are sub-jects that have been the main focus of the SLAM research community over the pastfive years.II.History of the SLAM Problem The genesis of the probabilistic SLAM problem occurred at the1986IEEE Robotics and Automation Conference held in San Francisco.This was a time when probabilis-tic methods were only just beginning to be introduced into both robotics and AI.A number of researchers had been looking at applying estimation-theoretic methods to mapping and localisation problems;these included Peter Cheeseman,Jim Crowley,and Hugh Durrant-Whyte.Over the course of the conference many paper table cloths and napkins werefilled with long discussions about consistent mapping.Along the way,Raja Chatila,Oliver Faugeras, Randal Smith and others also made useful contributions to the conversation.The result of this conversation was a recognition that consistent probabilistic mapping was a fundamental prob-lem in robotics with major conceptual and computational issues that needed to be addressed.Over the next few years a number of key papers were produced.Work by Smith and Cheesman[39]and Durrant-Whyte[17]established a statistical basis for describing relationships between land-marks and manipulating geometric uncertainty.A key el-ement of this work was to show that there must be a high degree of correlation between estimates of the location of different landmarks in a map and that indeed these corre-lations would grow with successive observations.At the same time Ayache and Faugeras[1]were under-taking early work in visual navigation,Crowley[9]and Chatila and Laumond[6]in sonar-based navigation of mo-bile robots using Kalmanfilter-type algorithms.These two strands of research had much in common and resulted soon after in the landmark paper by Smith,Self and Cheese-man[40].This paper showed that as a mobile robot moves through an unknown environment taking relative observa-tions of landmarks,the estimates of these landmarks are all necessarily correlated with each other because of the common error in estimated vehicle location[27].The im-plication of this was profound:A consistent full solution to the combined localisation and mapping problem wouldrequire a joint state composed of the vehicle pose and every landmark position,to be updated following each landmark observation.In turn,this would require the estimator to employ a huge state vector(of order the number of land-marks maintained in the map)with computation scaling as the square of the number of landmarks.Crucially,this work did not look at the convergence prop-erties of the map or its steady-state behavior.Indeed,it was widely assumed at the time that the estimated map errors would not converge and would instead exhibit a ran-dom walk behavior with unbounded error growth.Thus, given the computational complexity of the mapping prob-lem and without knowledge of the convergence behavior of the map,researchers instead focused on a series of approxi-mations to the consistent mapping problem solution which assumed or even forced the correlations between landmarks to be minimized or eliminated so reducing the fullfilter to a series of decoupled landmark to vehiclefilters([28],[38] for example).Also for these reasons,theoretical work on the combined localisation and mapping problem came to a temporary halt,with work often focused on either mapping or localisation as separate problems.The conceptual break-through came with the realisation that the combined mapping and localisation problem,once formulated as a single estimation problem,was actually convergent.Most importantly,it was recognised that the correlations between landmarks,that most researchers had tried to minimize,were actually the critical part of the problem and that,on the contrary,the more these corre-lations grew,the better the solution.The structure of the SLAM problem,the convergence result and the coining of the acronym‘SLAM’wasfirst presented in a mobile robot-ics survey paper presented at the1995International Sym-posium on Robotics Research[16].The essential theory on convergence and many of the initial results were developed by Csorba[11],[10].Several groups already working on mapping and localisation,notably at MIT[29],Zaragoza [5],[4],the ACFR at Sydney[20],[45]and others[7],[13], began working in earnest on SLAM1applications in indoor, outdoor and sub-sea environments.At this time,work focused on improving computational efficiency and addressing issues in data association or‘loop closure’.The1999International Symposium on Robot-ics Research(ISRR’99)[23]was an important meeting point where thefirst SLAM session was held and where a degree of convergence between the Kalman-filter based SLAM methods and the probabilistic localisation and map-ping methods introduced by Thrun[42]was achieved.The 2000IEEE ICRA Workshop on SLAM attractedfifteen re-searchers and focused on issues such as algorithmic com-plexity,data association and implementation challenges. The following SLAM workshop at the2002ICRA attracted 150researchers with a broad range of interests and appli-cations.The2002SLAM summer school hosted by Hen-rik Christiansen at KTH in Stockholm attracted all the 1Also called Concurrent Mapping and Localisation(CML)at this time.key researchers together with some50PhD students from around the world and was a tremendous success in build-ing thefield.Interest in SLAM has grown exponentially in recent years,and workshops continue to be held at both ICRA and IROS.The SLAM summer school ran in2004 in Tolouse and will run at Oxford in2006.III.Formulation and Structure of the SLAM problemSLAM is a process by which a mobile robot can build a map of an environment and at the same time use this map to deduce it’s location.In SLAM both the trajectory of the platform and the location of all landmarks are esti-mated on-line without the need for any a priori knowledge of location.A.PreliminariesFig.1.The essential SLAM problem.A simultaneous estimate of both robot and landmark locations is required.The true locations are never known or measured directly.Observations are made between true robot and landmark locations.See text for details.Consider a mobile robot moving through an environment taking relative observations of a number of unknown land-marks using a sensor located on the robot as shown in Figure1.At a time instant k,the following quantities are defined:•x k:The state vector describing the location and orien-tation of the vehicle.•u k:The control vector,applied at time k−1to drive the vehicle to a state x k at time k.•m i:A vector describing the location of the i th landmark whose true location is assumed time invariant.•z ik:An observation taken from the vehicle of the location of the i th landmark at time k.When there are multiple landmark observations at any one time or when the specific landmark is not relevant to the discussion,the observation will be written simply as z k.In addition,the following sets are also defined:•X 0:k ={x 0,x 1,···,x k }={X 0:k −1,x k }:The history of vehicle locations.•U 0:k ={u 1,u 2,···,u k }={U 0:k −1,u k }:The history of control inputs.•m ={m 1,m 2,···,m n }The set of all landmarks.•Z 0:k ={z 1,z 2,···,z k }={Z 0:k −1,z k }:The set of all landmark observations.B.Probabilistic SLAMIn probabilistic form,the Simultaneous Localisation and Map Building (SLAM)problem requires that the probabil-ity distributionP (x k ,m |Z 0:k ,U 0:k ,x 0)(1)be computed for all times k .This probability distribution describes the joint posterior density of the landmark lo-cations and vehicle state (at time k )given the recorded observations and control inputs up to and including time k together with the initial state of the vehicle.In gen-eral,a recursive solution to the SLAM problem is de-sirable.Starting with an estimate for the distribution P (x k −1,m |Z 0:k −1,U 0:k −1)at time k −1,the joint pos-terior,following a control u k and observation z k ,is com-puted using Bayes Theorem.This computation requires that a state transition model and an observation model are defined describing the effect of the control input and observation respectively.The observation model describes the probability of making an observation z k when the vehicle location and landmark locations are known,and is generally described in the formP (z k |x k ,m ).(2)It is reasonable to assume that once the vehicle locationand map are defined,observations are conditionally inde-pendent given the map and the current vehicle state.The motion model for the vehicle can be described in terms of a probability distribution on state transitions in the formP (x k |x k −1,u k )(3)That is,the state transition is assumed to be a Markovprocess in which the next state x k depends only on the immediately proceeding state x k −1and the applied control u k ,and is independent of both the observations and the map.The SLAM algorithm is now implemented in a standard two-step recursive (sequential)prediction (time-update)correction (measurement-update)form:Time-updateP (x k ,m |Z 0:k −1,U 0:k ,x 0)=P (x k |x k −1,u k )×P (x k −1,m |Z 0:k −1,U 0:k −1,x 0)d x k −1(4)Measurement UpdateP (x k ,m |Z 0:k ,U 0:k ,x 0)=P (z k |x k ,m )P (x k ,m |Z 0:k −1,U 0:k ,x 0)P (z k |Z 0:k −1,U 0:k )(5)Equations 4and 5provide a recursive procedure for calcu-lating the joint posterior P (x k ,m |Z 0:k ,U 0:k ,x 0)for the robot state x k and map m at a time k based on all observa-tions Z 0:k and all control inputs U 0:k up to and including time k .The recursion is a function of a vehicle model P (x k |x k −1,u k )and an observation model P (z k |x k ,m ).It is worth noting that the map building problem may be formulated as computing the conditional density P (m |X 0:k ,Z 0:k ,U 0:k ).This assumes that the location of the vehicle x k is known (or at least deterministic)at all times,subject to knowledge of initial location.A map m is then constructed by fusing observations from differ-ent locations.Conversely,the localisation problem may be formulated as computing the probability distribution P (x k |Z 0:k ,U 0:k ,m ).This assumes that the landmark lo-cations are known with certainty and the objective is to compute an estimate of vehicle location with respect to these landmarks.C.Structure of Probabilistic SLAMTo simplify the discussion in this section we will drop the conditioning on historical variables in Equation 1and write the required joint posterior on map and vehicle location as P (x k ,m |z k )and even P (x k ,m )as the context permits.The observation model P (z k |x k ,m )makes explicit the dependence of observations on both the vehicle and land-mark locations.It follows that the joint posterior can not be partitioned in the obvious mannerP (x k ,m |z k )=P (x k |z k )P (m |z k ),and indeed it is well known from the early papers on consis-tent mapping [39],[17]that a partition such as this leads to inconsistent estimates.However,the SLAM problem has more structure than is immediately obvious from these Equations.Referring again to Figure 1,it can be seen that much of the error between estimated and true landmark loca-tions is common between landmarks and is in fact due to a single source;errors in knowledge of where the robot is when landmark observations are made.In turn,this im-plies that the errors in landmark location estimates are highly correlated.Practically,this means that the relative location between any two landmarks,m i −m j ,may be known with high accuracy,even when the absolute loca-tion of a landmark m i is quite uncertain.In probabilistic form this means that the joint probability density for the pair of landmarks P (m i ,m j )is highly peaked even when the marginal densities P (m i )may be quite dispersed.The most important insight in SLAM was to realize that the correlations between landmark estimates increasemonotonically as more and more observations are Practically,this means that knowledge of the relative tion of landmarks always improves and never gardless of robot motion.In probabilistic terms,this that the joint probability density on all landmarks becomes monotonically more peaked as moreare made.This convergence occurs because the observations by the robot can be considered as‘nearly measurements of the relative location between Referring again to Figure1,consider the robot atx k observing the two landmarks m i and m j.The location of observed landmarks is clearlythe coordinate frame of the vehicle and successivevations from thisfixed location would yield further pendent measurements of the relative relationship between landmarks.Now,as the robot moves to location x k+1, it again observes landmark m j this allows the estimated location of the robot and landmark to be updated rela-tive to the previous location x k.In turn this propagates back to update landmark m i even though this landmark is not seen from the new location.This occurs because the two landmarks are highly correlated(their relative lo-cation is well known)from previous measurements.Fur-ther,the fact that the same measurement data is used to update these two landmarks makes them more correlated. The term‘nearly independent’measurement is appropriate because the observation errors will be correlated through successive vehicle motions.Also note that in Figure1at location x k+1the robot observes two new landmarks rel-ative to m j.These new land-marks are thus immediately linked or correlated to the rest of the ter update to these landmarks will also update landmark m j and through this landmark m i and so on.That is,all landmarks end up forming a network linked by relative location or cor-relations whose precision or value increases whenever an observation is made.This process can be visualized(Figure2)as a network of springs connecting all landmarks together,or as a rubber sheet in which all landmarks are embedded.An observa-tion in a neighbourhood acts like a displacement to spring system or rubber sheet such that it’s effect is great in the neighbourhood and,dependent on local stiffness(correla-tion)properties,diminishes with distance to other land-marks.As the robot moves through this environment and takes observations of the landmarks,the the springs be-come increasingly(and monotonically)stiffer.In the limit, a rigid map of landmarks or an accurate relative map of the environment is obtained.As the map is built,the lo-cation accuracy of the robot measured relative to the map is bounded only by the quality of the map and relative measurement sensor.In the theoretical limit,robot rel-ative location accuracy becomes equal to the localisation accuracy achievable with a given map.2These results have only been proved for the linear Gaussian case [14].Formal proof for the more general probabilistic case remains an open problem.environment,correlations increase(red links become thicker).As landmarks are observed and estimated locations are corrected,and these changes are propagated through the spring network.Note,the robot itself is correlated to the map.IV.Solutions to the SLAM Problem Solutions to the probabilistic SLAM problem involve finding an appropriate representation for the observation model Equation2and motion model Equation3which al-lows efficient and consistent computation of the prior and posterior distributions in Equations4and5.By far the most common representation is in the form of a state-space model with additive Gaussian noise,leading to the use of the extended Kalmanfilter(EKF)to solve the SLAM problem as described in Section IV-A.One important al-ternative representation is to describe the vehicle motion model in Equation3as a set of samples of a more gen-eral non-Gaussian probability distribution.This leads to the use of the Rao-Blackwellised particlefilter,or Fast-SLAM algorithm,to solve the SLAM problem as described in Section IV-B.While EKF-SLAM and FastSLAM are the two most important solution methods,newer alternatives, which offer much potential,have been proposed including the use of the information-state form[43].These are dis-cussed further in Part II of this tutorial.A.EKF-SLAMThe basis for the EKF-SLAM method is to describe the vehicle motion in the formP(x k|x k−1,u k)⇐⇒x k=f(x k−1,u k)+w k,(6)where f(·)models vehicle kinematics and where w k are additive,zero mean uncorrelated Gaussian motion distur-bances with covariance Q k.The observation model is de-scribed in the formP(z k|x k,m)⇐⇒z(k)=h(x k,m)+v k,(7)where h(·)describes the geometry of the observation and where v k are additive,zero mean uncorrelated Gaussian observation errors with covariance R k.With these definitions the standard EKF method[31], [14]can be applied to compute the meanˆx k|kˆm k=Ex km|Z0:k,and covarianceP k|k=P xx P xmP T xm P mmk|k=Ex k−ˆx km−ˆm kx k−ˆx km−ˆm kT|Z0:kof the joint posterior distribution P(x k,m|Z0:k,U0:k,x0) from:Time-updateˆx k|k−1=f(ˆx k−1|k−1,u k)(8) P xx,k|k−1=∇f P xx,k−1|k−1∇f T+Q k(9)where∇f is the Jacobian of f evaluated at the estimate ˆx k−1|k−1.There is generally no need to perform a time-update for stationary landmarks3.Observation-updateˆx k|kˆm k=ˆx k|k−1ˆm k−1+W kz(k)−h(ˆx k|k−1,ˆm k−1)(10)P k|k=P k|k−1−W k S k W Tk(11)whereS k=∇hP k|k−1∇h T+R kW k=P k|k−1∇h T S−1kand where∇h is the Jacobian of h evaluated atˆx k|k−1and ˆm k−1.This EKF-SLAM solution is very well known and inherits many of the same benefits and problems as the standard EKF solutions to navigation or tracking problems.Four of the key issues are briefly discussed here: Convergence:In the EKF-SLAM problem,convergence of the map is manifest in the monotonic convergence of the determinant of the map covariance matrix P mm,k,and all land-mark pair sub-matrices,toward zero.The individual land-mark variances converge toward a lower bound deter-mined by initial uncertainties in robot position and obser-vations.The typical convergence behaviour of landmark location variances is shown in Figure3(from[14]). Computational Effort:The observation update step re-quires that all landmarks and the joint covariance matrix be updated every time an observation is made.Naively, this means computation grows quadratically with the num-ber of landmarks.There has been a great deal of work un-dertaken in developing efficient variants of the EKF-SLAM solution and real-time implementations with many thou-sands of landmarks have been demonstrated[21],[29].Ef-ficient variants of the EKF-SLAM algorithm are discussed in Part II of this tutorial.3However,a time-update is necessary for landmarks that may move [44].4050607080901001100.511.522.5Time (sec)StandardDeviationinX(m)Fig.3.The convergence in landmark uncertainty.The plot shows a time history of standard deviations of a set of landmark locations.A landmark is initially observed with uncertainty inherited from the robot location and observation.Over time,the standard deviations reduce monotonically to a lower bound.New landmarks are acquired during motion(from[14]).Data Association:The standard formulation of the EKF-SLAM solution is especially fragile to incorrect associ-ation of observations to landmarks[35].The‘loop-closure’problem,when a robot returns to re-observe landmarks af-ter a large traverse,is especially difficult.The association problem is compounded in environments where landmarks are not simple points and indeed look different from differ-ent view-points.Current work in this area will be described in Part II of this tutorial.Non-linearity:EKF-SLAM employs linearised models of non-linear motion and observation models and so inher-its many caveats.Non-linearity can be a significant prob-lem in EKF-SLAM and leads to inevitable,and sometimes dramatic,inconsistency in solutions[24].Convergence and consistency can only be guaranteed in the linear case. B.Rao-Blackwellised FilterThe FastSLAM algorithm,introduced by Montemerlo et al.[32],marked a fundamental conceptual shift in the de-sign of recursive probabilistic SLAM.Previous efforts fo-cused on improving the performance of EKF-SLAM,while retaining its essential linear Gaussian assumptions.Fast-SLAM with its basis on recursive Monte Carlo sampling, or particlefiltering,was thefirst to directly represent the non-linear process model and non-Gaussian pose distribu-tion.4This approach was influenced by earlier probabilistic mapping experiments of Murphy[34]and Thrun[41]. The high dimensional state-space of the SLAM prob-lem makes direct application of particlefilters compu-tationally infeasible.However,it is possible to reduce the sample-space by applying Rao-Blackwellisation(R-B), 4Note,FastSLAM still linearises the observation model,but this is typically a reasonable approximation for range-bearing measurements when the vehicle pose is known.whereby a joint state is partitioned according to the prod-uct rule P(x1,x2)=P(x2|x1)P(x1)and,if P(x2|x1) can be represented analytically,only P(x1)need be sam-pled x(i)1∼P(x1).The joint distribution,therefore,is represented by the set{x(i)1,P(x2|x(i)1)}N i and statistics such as the marginalP(x2)≈1NNiP(x2|x(i)1)can be obtained with greater accuracy than is possible by sampling over the joint space.The joint SLAM state may be factored into a vehicle component and a conditional map component.P(X0:k,m|Z0:k,U0:k,x0)=P(m|X0:k,Z0:k)P(X0:k|Z0:k,U0:k,x0).(12) Here the probability distribution is on the trajectory X0:k rather than the single pose x k because,when conditioned on the trajectory,the map landmarks become independent (see Figure4).This is a key property of FastSLAM,and the reason for its speed;the map is represented as a set of independent Gaussians,with linear complexity,rather than a joint map covariance with quadratic complexity.Fig.4.A graphical model of the SLAM algorithm.If the history of pose states are known exactly then,since the observations are condi-tionally independent,the map states are also independent.For Fast-SLAM,each particle defines a different vehicle trajectory hypothesis. The essential structure of FastSLAM,then,is a Rao-Blackwellised state,where the trajectory is represented by weighted samples and the map is computed analytically. Thus,the joint distribution,at time k,is represented by theset{w(i)k ,X(i)0:k,P(m|X(i)0:k,Z0:k)}N i,where the map accom-panying each particle is composed of independent Gaussiandistributions P(m|X(i)0:k ,Z0:k)=MjP(m j|X(i)0:k,Z0:k).Recursive estimation is performed by particlefiltering for the pose states,and the EKF for the map states.Updating the map,for a given trajectory particle X(i)0:k ,istrivial.Each observed landmark is processed individually as an EKF measurement update from a known pose(see Figure5).Unobserved landmarks are unchanged.Propa-gating the pose particles,on the other hand,is more com-plex,as we discuss below.We forego giving a background on particlefilters,except to say that it is derived from a recursive form of sampling Fig.5.A single realisation of robot trajectory in the FastSLAM algo-rithm.The ellipsoids show the proposal distribution for each update stage,from which a robot pose is sampled and,assuming this poseis perfect,the observed landmarks are updated.Thus,the map fora single particle is governed by the accuracy of the trajectory.Many such trajectories provide a probabilistic model of robot location. known as sequential important sampling(SIS)[15],which actually samples from a joint state history,but“telescopes”the joint into a recursion via the product rule.P(x0,x1,...,x T|Z0:T)=P(x0|Z0:T)P(x1|x0,Z0:T)...P(x T|X0:T−1,Z0:T). At each time-step k,particles are drawn from a proposal distributionπ(x k|X0:k−1,Z0:k),which approximates the true distribution P(x k|X0:k−1,Z0:T),and the samples are given importance weights to compensate for any discrep-ancy.The approximation error grows with time(and in-herent joint state-space),increasing the variation in sam-ple weights,degrading statistical accuracy.A resampling step reinstates uniform weighting,but causes loss of histor-ical particle information.This leads to a crucial property: SIS with resampling can produce reasonable statistics only for systems that“exponentially forget”their past[8](i.e., systems whose process noise cause the state at time k to become increasingly independent of preceding states).The general form of a R-B particlefilter for SLAM is as follows.We assume that,at time k−1,the joint state is represented by{w(i)k−1,X(i)0:k−1,P(m|X(i)0:k−1,Z0:k−1)}N i.1.For each particle,compute a proposal distribution,con-ditioned on the specific particle history,and draw a sample from itx(i)k∼π(x k|X(i)0:k−1,Z0:k,u k).(13) This new sample is(implicitly)joined to the particle his-tory X(i)0:k=X(i)0:k−1,x(i)k.2.Weight samples according to the importance functionw(i)k=w(i)k−1P(z k|X(i)0:k,Z0:k−1)P(x(i)k|x(i)k−1,u k)π(x(i)k|X(i)0:k−1,Z0:k,u k).(14)The numerator terms of this equation are the observation model and the motion model,respectively.The former。
6d位姿估计算法
6D位姿估计算法是计算机视觉领域的一个基本问题，用于估计物体在三维空间中的位置和朝向。
根据输入数据类型的不同，6D位姿估计算法可以分为以下几类：
1. 基于RGB图像的6D位姿估计：这种方法首先从输入的RGB图像中提取特征，然后与已有的3D模型进行匹配，通过PnP （Perspective-n-Point）算法建立2D-3D坐标的对应关系，从而估计目标物体的6D位姿。
2. 基于点云的6D位姿估计：这种方法直接使用点云数据作为输入，通过点云配准或深度学习技术来估计物体的6D位姿。
3. RGB+点云融合的6D位姿估计：这种方法结合了RGB图像和点云数据，利用两者互补的信息来提高6D位姿估计的准确性和鲁棒性。
不同的6D位姿估计算法有各自的优缺点和适用场景。
在选择适合的方法时，需要根据具体的应用场景、数据类型和精度要求等因素进行综合考虑。
slam建图学习计划
slam建图学习计划1. 学习目标我们的学习目标是建立对Slam建图技术的深入理解，包括算法原理、数学基础和实际应用。
具体包括以下几个方面：（1）了解Slam的基本原理和分类，包括基于滤波、基于图优化和基于深度学习的方法。
（2）掌握Slam建图中常用的算法，包括扩展卡尔曼滤波（EKF）、粒子滤波、图优化和深度学习等。
（3）熟悉Slam建图的数学基础，包括线性代数、概率论和最优化方法。
（4）了解Slam在实际应用中的挑战和解决方案，包括传感器选择、环境建模和实时性能等。
2. 学习内容为了达成以上学习目标，我们可以按照以下内容进行学习：（1）Slam基础知识学习Slam的基本原理和分类，包括基于特征、基于直接法和基于深度学习的方法。
可以阅读相关的论文和教材，例如《Probabilistic Robotics》、《SLAM for Dummies》，并参与Slam建图相关的在线课程和讨论。
（2）Slam算法学习Slam建图中的常用算法，包括EKF、粒子滤波、图优化和深度学习等。
可以使用开源Slam库，例如Gmapping、Cartographer和LSD-slam，进行实际的算法实现和调试。
（3）数学基础巩固线性代数、概率论和最优化方法的数学基础，这些知识在Slam建图中非常重要。
可以参考相关的教材和课程，例如《线性代数应用》、《概率图模型》和《最优化方法》，并进行相关的习题练习。
（4）实际应用了解Slam在自动驾驶、无人机、虚拟现实和室内导航等领域的实际应用。
可以阅读相关的研究论文和技术博客，了解不同领域的Slam建图技术和挑战。
3. 实践项目为了加强对Slam建图技术的理解和应用，我们可以选择一个实际的项目来进行实践。
例如使用激光雷达和相机数据，实现一个室内建图和导航系统。
可以使用开源的Slam库和传感器套件，例如ROS、Hector Slam和ZED相机，实现一个完整的Slam建图系统。
4. 教学反馈在学习过程中，我们建议定期进行教学反馈，包括参加线下或线上的Slam建图研讨会、参与Slam建图竞赛和分享自己的项目经验。
6d位姿估计数据集制作方法
6d位姿估计数据集制作方法6D位姿估计数据集制作方法主要包括以下几个步骤：1. 采集数据：首先，需要采集一定数量的RGB图像和对应的3D模型。
这些图像可以来自于不同的场景、光照条件和视角，以保证数据集的丰富性和多样性。
同时，对应的3D模型可以是预先扫描或者建模得到的。
2. 标注数据：对于每幅图像，需要标注其中目标物体的位置和姿态。
这些标注数据可以通过专业工具（如PASCAL VOC、COCO等）进行手动标注，也可以通过半自动或自动标注方法（如SfM、Multi-View Stereo 等）生成。
标注数据包括物体的2D边界框和3D位置姿态信息。
3. 制作训练集和测试集：将采集到的图像和对应的标注数据分为训练集和测试集。
训练集用于训练模型，测试集用于评估模型的性能。
为了保证评估的公正性，测试集的数据应与训练集的数据具有相似的分布。
4. 数据增强：为了提高模型的泛化能力，可以对训练集数据进行增强。
数据增强方法包括旋转、缩放、翻转、剪裁等。
需要注意的是，在数据增强过程中，需保持物体的3D姿态不变。
5. 归一化：将图像和标注数据进行归一化处理，使其在同一尺度下进行计算。
归一化方法包括缩放图像、标准化关键点坐标等。
6. 制作数据集：将处理好的图像、标注数据以及对应的3D模型组织成数据集。
数据集可以采用常用的格式如TensorFlow、PyTorch等进行存储和处理。
7. 模型训练：使用制作好的数据集训练6D位姿估计模型。
模型可以是基于特征匹配、模板匹配的方法，也可以是基于深度卷积网络（CNN）的方法。
训练过程中需要优化模型的损失函数，以提高模型在测试集上的性能。
8. 模型评估：在训练过程中或训练完成后，使用测试集评估模型的性能。
评估指标可以包括平均准确度（MPJPE）、均方根误差（RMSE）等。
根据评估结果，可以调整模型参数或数据集制作方法，以提高模型性能。
通过以上步骤，可以制作一个适用于6D位姿估计任务的数据集。
复杂场景点云数据的6D位姿估计深度学习网络
复杂场景点云数据的6D位姿估计深度学习网络
陈海永;李龙腾;陈鹏;孟蕊
【期刊名称】《电子与信息学报》
【年(卷),期】2022(44)5
【摘要】针对工业上常见的弱纹理、散乱摆放复杂场景下点云目标机器人抓取问题,该文提出一种6D位姿估计深度学习网络。
首先,模拟复杂场景下点云目标多姿态随机摆放的物理环境,生成带真实标签的数据集;进而,设计了6D位姿估计深度学习网络模型,提出多尺度点云分割网络(MPCS-Net),直接在完整几何点云上进行点云实例分割,解决了对RGB信息和点云分割预处理的依赖问题。
然后,提出多层特征姿态估计网(MFPE-Net),有效地解决了对称物体的位姿估计问题。
最后,实验结果和分析证实了,相比于传统的点云配准方法和现有的切分点云的深度学习位姿估计方法,所提方法取得了更高的准确率和更稳定性能,并且在估计对称物体位姿时有较强的鲁棒性。
【总页数】11页(P1591-1601)
【作者】陈海永;李龙腾;陈鹏;孟蕊
【作者单位】河北工业大学
【正文语种】中文
【中图分类】TP391.4
【相关文献】
1.复杂场景下基于C-SHOT特征的3D物体识别与位姿估计
2.基于位置依赖的密集融合的6D位姿估计方法
3.基于位置依赖的密集融合的6D位姿估计方法
4.基于深度学习的物体点云六维位姿估计方法
5.堆叠散乱目标的6D位姿估计和无序分拣
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融合残差注意力和标准偏差的6D姿态细化网络
融合残差注意力和标准偏差的6D姿态细化网络
邓江;陈姚节;张梦杰
【期刊名称】《计算机系统应用》
【年(卷),期】2024(33)3
【摘要】在6D物体姿态估计领域中,现有算法往往难以实现对目标物体精准且鲁棒的姿态估计.为解决该问题,提出了一种结合残差注意力、混合空洞卷积和标准差信息的物体6D姿态细化网络.首先,在Gen6D图片特征提取网络中,采用混合空洞卷积模块替换传统卷积模块,以此扩大感受野、加强全局特征捕获能力.接着,在3D 卷积神经网络中,加入残差注意力模块,这有助于区分特征通道的重要程度,进而在提取关键特征的同时,减少浅层特征的丢失.最后,在平均距离损失函数中,引入了标准差信息,从而使模型能够区分物体的更多姿态信息.实验结果显示,所提出的网络在LINEMOD数据集和GenMOP数据集上的ADD指标分别达到了68.79%和56.03%.与Gen6D网络相比,ADD指标分别提升了1.78个百分点和5.64个百分点,这一结果验证了所提出的网络能够显著提升6D姿态估计的准确性.
【总页数】8页(P187-194)
【作者】邓江;陈姚节;张梦杰
【作者单位】武汉科技大学计算机科学与技术学院
【正文语种】中文
【中图分类】TP3
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1.融合多重注意力机制残差网络的血细胞识别
2.基于特征融合和注意力机制的物体6D姿态估计算法
3.融合残差网络与注意力机制的草莓检测
4.融合注意力与残差网络的石油管材失效宏观影像智能识别方法
5.用于红外与可见光图像融合的注意力残差密集融合网络
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1
ECMR 2007 –SLAM Tutorial
6D SLAM
Dr. Andreas Nüchter
Institute of Computer Science
Knowledge-Based-Systems Research Group
University of Osnabrück
andreas@nuechti.de http://www.nuechti.de
Pose via
wheel revolutions
Measured data,
camera or scanner Map represented as grid or that contains features
Mounted on mobile robots for 3D collision avoidance
http://www.rts.uni-hannover.de/index.php/%C3%9Cbersicht_der_m%C3%B6glichen_Scannerkonfigurationen
•Professional 3D scanners
–Structured
light (close
range)
–pulsed laser vs. time-of-flight (mid and long range)
What you should learn now, using the show program Most robotic data sets acquired by a
Viewing a single indoor 3D scan, setting a maximal distance
–s 1 –e 1 dat_schillerplatz
Convert to matrix , then
works in 3 translation plus 3 rotation dimensions
6D SLAM with closed loop detection and global relaxation.
first term! (The
).
with,then for
is
Rewrite
Proof:
Rewrite using the trace of a matrix Lemma: For all positiv definite matrices
for
with
,then is
of is
and 3 x 3 and a diagonal matrix and
lemma it is
Therefore maximizes for with
,then with is
K-d trees for searching in logarithmic time
⇐Backtracking Approximation in the ANN package represents a method for not-evaluating leafs, taking small errors into account.
How to split a k-d tree during construction?
Cells may have an arbitrary aspect ratio
Best performance is achieved by the so-called optimized
bin/slam6D –s 0 –e 3 –m 3000 –-algo=2 dat1
bin/slam6D –s 0 –e 15 –m 3000 –-algo=2 dat2
extrapolation and ICP using reduced data (indoors)
where models random Gaussian noise, added to the unkown exact pose and the co-variance matrix of the overlapping scans computed from
extrapolation and ICP and
loop detection and global relaxation bin/slam6D –s 0 –e 75 –r 10 –i 100 –d 250
-I 50 –-cldist=750 –D 500 dat3。