### 机器人代写|SLAM代写机器人导航代考|Joint Compatibility Branch and Bound

statistics-lab™ 为您的留学生涯保驾护航 在代写SLAM方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写SLAM代写方面经验极为丰富，各种代写SLAM相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器人代写|SLAM代写机器人导航代考|Joint Compatibility Branch and Bound

If multiple observations are gathered per control, the maximum likelihood approach will treat each data association decision as a independent problem. However, because data association ambiguity is caused in part by robot pose uncertainty, the data associations of simultaneous observations are correlated. Considering the data association of each of the observations separately also ignores the issue of mutual exclusion. Multiple observations cannot be associated with the same landmark during a single time step.

Neira and Tardos [68] showed that both of these problems can be remedied by considering the data associations of all of the observations simultaneously, much like the Local Map Sequencing algorithm does. Their algorithm, called Joint Compatibility Branch and Bound (JCBB), traverses the Interpretation Tree [35], which is the tree of all possible joint correspondences. Different joint data association hypotheses are compared using joint compatibility, a measure of the probability of the set of observations occurring together. In the EKF framework, this can be computed by finding the probability of the joint innovations of the observations. Clearly, considering joint correspondences comes at some computational cost, because an exponential number of different hypotheses must be considered. However, Neira and Tardos showed that many of these hypotheses can be excluded without traversing the entire tree.

## 机器人代写|SLAM代写机器人导航代考|Combined Constraint Data Association

Bailey [1] presented a data association algorithm similar to JCBB called Combined Constraint Data Association (CCDA). Instead of building a tree of joint correspondences, CCDA constructs a undirected graph of data association constraints, called a “Correspondence Graph”. Each node in the graph. represents a candidate pairing of observed features and landmarks, possibly determined using a nearest neighbor test. Edges between the nodes represent joint compatibility between pairs of data associations. The algorithm picks the set of joint data associations that correspond to the largest clique in the correspondence graph. The results of JCBB and CCDA should be similar, however the CCDA algorithm is able to determine viable data associations when the pose of the robot relative to the map is completely unknown.

Scan matching $[54]$ is a data association method that based on a modified version of the Iterative Closest Point (ICP) algorithm [4]. This algorithm alternates between a step in which correspondences between data are identified, and a step in which a new robot path is recovered from the current correspondences. This iterative optimization is similar in spirit to Expectation Maximization (EM) [17] and RANSAC [27]. First, a locally consistent map is built using scan-matching $[39]$, a maximum likelihood mapping approach. Next, observations are matched between different sensor scans using a distance metric. Based on the putative correspondences, a new set of robot poses is derived. This alternating process is iterated several times until some convergence criterion is reached. This process has shown significant promise for the data association problems encountered in environments with very large loops.

## 机器人代写|SLAM代写机器人导航代考|Multiple Hypothesis Tracking

Thus far, all of the data association algorithms presented all choose a single data association hypothesis to be fed into an EKF, or approximate EKF algorithm. There are a few algorithms that maintain multiple data association hypotheses over time. This is especially useful if the correct data association of an observation cannot be inferred from a single measurement. One such approach in the target tracking literature is the Multiple Hypothesis Tracking or MHT algorithm [77]. MHT maintains a set of hypothesized tracks of multiple targets. If a particular observation has multiple, valid data association

interpretation s, new hypotheses are created according to each hypothesis. In order to keep the number of hypotheses from expanding without bound, heuristics are used to prune improbable hypotheses from the set over time.
Maintaining multiple EKF hypotheses for SLAM is unwieldy because each EKF maintains a belief over robot pose and the entire map. Nebot et al. [67] have developed a similar technique that “pauses”. map-building when data association becomes ambiguous, and performs multi-hypothesis localization using a particle filter until the ambiguity is resolved. Since map building is not performed when there is data association ambiguity, the multiple hypotheses are over robot pose, which is a low-dimensional quantity. However, this approach only works if data association ambiguity occurs sporadically. This can be useful for resolving occasional data association problems when closing loops, however the algorithm will never spend any time mapping if the ambiguity is persistent.

## 机器人代写|SLAM代写机器人导航代考|Joint Compatibility Branch and Bound

Neira 和 Tardos [68] 表明，这两个问题都可以通过同时考虑所有观察结果的数据关联来解决，就像局部地图排序算法一样。他们的算法称为联合兼容性分支定界 (JCBB)，它遍历解释树 [35]，它是所有可能的联合对应的树。使用联合兼容性来比较不同的联合数据关联假设，联合兼容性是一组观测值一起发生的概率的度量。在 EKF 框架中，这可以通过找到观察的联合创新的概率来计算。显然，考虑联合对应需要一些计算成本，因为必须考虑指数数量的不同假设。然而，

## 机器人代写|SLAM代写机器人导航代考|Combined Constraint Data Association

Bailey [1] 提出了一种类似于 JCBB 的数据关联算法，称为组合约束数据关联 (CCDA)。CCDA 不是构建联合对应树，而是构建数据关联约束的无向图，称为“对应图”。图中的每个节点。表示观察到的特征和地标的候选配对，可能使用最近邻测试确定. 节点之间的边表示数据关联对之间的联合兼容性。该算法选择与对应图中最大集团相对应的联合数据关联集。JCBB 和 CCDA 的结果应该相似，但是当机器人相对于地图的位姿完全未知时，CCDA 算法能够确定可行的数据关联。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。