没有合适的资源？快使用搜索试试~ 我知道了~

首页Local Accuracy and Global Consistency for Efficient Visual SLAM

Local Accuracy and Global Consistency for Efficient Visual SLAM

slam

5星 · 超过95%的资源需积分: 15 33 下载量 114 浏览量更新于2023-05-24 2 收藏 28.43MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

213页

Local Accuracy and Global Consistency for Efficient Visual SLAM

资源详情

资源推荐

Imperial College London

Department of Computing

Local Accuracy and Global

Consistency for Eﬃcient Visual SLAM

Hauke Strasdat

October 2012

Supervised by Dr. Andrew Davison

Submitted in part fulﬁlment of the requirements for the degree of PhD in

Computing and the Diploma of Imperial College London. This thesis is entirely

my own work, and, except where otherwise indicated, describes my own research.

To So-Rim Lee

Abstract

This thesis is concerned with the problem of Simultaneous Localisation and

Mapping (SLAM) using visual data only. Given the video stream of a moving

camera, we wish to estimate the structure of the environment and the motion

of the device most accurately and in real-time.

Two eﬀective approaches were presented in the past. Filtering methods

marginalise out past poses and summarise the information gained over time

with a probability distribution. Keyframe methods rely on the optimisation

approach of bundle adjustment, but computationally must select only a small

number of past frames to process. We perform a rigorous comparison between

the two approaches for visual SLAM. Especially, we show that accuracy comes

from a large number of points, while the number of intermediate frames only

has a minor impact. We conclude that keyframe bundle adjustment is superior

to ﬁltering due to a smaller computational cost.

Based on these experimental results, we develop an eﬃcient framework for

large-scale visual SLAM using the keyframe strategy. We demonstrate that

SLAM using a single camera does not only drift in rotation and translation,

but also in scale. In particular, we perform large-scale loop closure correction

using a novel variant of pose-graph optimisation which also takes scale drift

into account. Starting from this two stage approach which tackles local mo-

tion estimation and loop closures separately, we develop a uniﬁed framework

for real-time visual SLAM. By employing a novel double window scheme, we

present a constant-time approach which enables the local accuracy of bundle

adjustment while ensuring global consistency. Furthermore, we suggest a new

scheme for local registration using metric loop closures and present several im-

provements for the visual front-end of SLAM. Our contributions are evaluated

exhaustively on a number of synthetic experiments and real-image data-set from

single cameras and range imaging devices.

剩余212页未读，继续阅读

Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity

cvpr2014年关于图像融合的文章Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity

Global and Local Contrast Adaptive Enhanceme

Global and Local Contrast Adaptive Enhancement for Non-uniform Illumination Color Images

Visual SLAM 15年发展历程(高清版)

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling is a research paper that proposes a new approach for video recommendation. The paper presents a hybrid sampling ...

The difference between accuracy and recall

Accuracy and recall are both metrics used to measure the performance of a classification model. Accuracy is the ratio of correctly predicted observations to the total number of observations. It ...

history key frames (history submap for loop closure)

History key frames, also known as history submaps or sliding windows, are a technique used in visual simultaneous localization and mapping (SLAM) to improve loop closure detection. In visual SLAM, ...

global accuracy_list global predict_list代码意思

这两行代码中的 `global` 关键字用于声明在函数内部使用的变量为全局变量，即使在函数内部对这些变量进行修改，也不会影响到函数外部的同名变量。 `accuracy_list` 和 `predict_list` 分别是两个全局变量，可以在...

激光slam如何改进

Laser SLAM (Simultaneous Localization and Mapping) is a popular technique for building real-time, high-accuracy maps of unknown environments using laser rangefinders. There are several ways to improve...

用英文写一段简述Scan-to-scan matching技术在SLAM激光雷达建图方面的贡献

Scan-to-scan matching technology has made ... This technology has greatly improved the accuracy and efficiency of SLAM systems, making them more reliable and effective in a variety of applications.

global accuracy_list

`global accuracy_list` is a global variable that refers to a list that can be accessed and modified from anywhere within the program. It is used to store the accuracy values of a machine learning ...

"""Get the accuracy and IoU for each class in the table format"""什么意思

这个任务的目标是计算每个类别的准确率（accuracy）和交并比（IoU），并将结果以表格的形式展示出来。表格的每一行代表一个类别，列标题则是准确率和交并比。例如，表格可能长这样： | 类别 | 准确率 | 交并比 | | ...

为解决遥感图像难分类问题，本文提出一种结合 CNN 与 Transformer 优点的图像分类方法。翻译成英文

### 回答1： In order to solve the problem of difficult classification of remote sensing images, ... It effectively integrates local and global information for improved accuracy in image classification.

Referring to Figure 6-24 , the following rules define timestamp capture: • A PTM Requester must update its stored t1 timestamp when transmitting a PTM Request Message, even if that transmission is a replay. • A PTM Responder must update its stored t2 timestamp when receiving a PTM Request Message, even if received TLP is a duplicate. • A PTM Responder must update its stored t3 timestamp when transmitting a PTM Response or ResponseD Message, even if that transmission is a replay. • A PTM Requester must update its stored t4 timestamp when receiving a PTM Response Message, even if received TLP is a duplicate. ◦ Timestamps must be based on the STP Symbol or Token that frames the TLP, as if observing the first bit of that Symbol or Token at the Port’s pins. Typically this will require an implementation specific adjustment to compensate for the inability to directly measure the time at the actual pins, as the time will commonly be measured at some internal point in the Rx or Tx path. The accuracy and consistency of this measurement are not bounded by this specification, but it is strongly recommended that the highest practical level of accuracy and consistency be achieved. 翻译一下

参考图6-24，以下规则定义了时间戳捕获：• PTM请求者在发送PTM请求消息时，必须更新其存储的t1时间戳，即使该传输是重播。• PTM响应者在接收PTM请求消息时，必须更新其存储的t2时间戳，即使接收到的TLP是重复的。...

One way to create a multivariate model is to: 1. Rank the independent variables by correlation, then create a linear model using the independent variable with the highest correlation. Measure the training and testing accuracy. 2. Add in the independent variable with the next highest correlation and create a new linear model. Measure the training and testing accuracy. 3. Stop when either accuracy score levels off or goes down.follow the steps above to create models with one, two and three independent variables, printing the training and testing accuracy each time. Note that you have to run _train_test_split_ for each model. Set the _random_state_ parameter in _train_test_split_ to 0 each time.

Sure! To create a multivariate model using the steps you mentioned, you can follow the... Remember to perform the train-test split for each model and set `random_state` to 0 to ensure consistent results.

def compile_train(model, data_train, data_label,data_val,label_val, deep=True): if (deep == True): import matplotlib.pyplot as plt model.compile(loss='binary_crossentropy', optimizer = 'Adam', metrics=['accuracy']) history = model.fit(data_train, data_label,validation_data=(data_val,label_val) ,epochs=10, batch_size=256,verbose=1) plt.plot(history.history['accuracy']) plt.plot(history.history['loss']) plt.title('Train Accuracy and Loss') plt.ylabel('Accuracy/Loss') plt.xlabel('Epoch') plt.legend(['accuracy', 'loss'], loc='upper left') plt.show() plt.plot(history.history['val_accuracy']) plt.plot(history.history['val_loss']) plt.title('Verification Accuracy and Loss') plt.ylabel('Accuracy/Loss') plt.xlabel('Epoch') plt.legend(['accuracy', 'loss'], loc='upper left') plt.show() print('Model Compiled and Trained') return model改成应用于模型本身

plt.title('Train Accuracy and Loss') plt.ylabel('Accuracy/Loss') plt.xlabel('Epoch') plt.legend(['accuracy', 'loss'], loc='upper left') plt.show() plt.plot(history.history['val_accuracy']) plt....

phlovexz

粉丝: 5
资源: 5

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

Local Accuracy and Global Consistency for Efficient Visual SLAM

Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity

Global and Local Contrast Adaptive Enhanceme

Visual SLAM 15年发展历程(高清版)

Fast and Robust Initialization for Visual-Inertial SLAM.pdf

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

accuracy and stability of numerical algorithms（英文版）

orbslam2+imu

yolo5 orbslam2 UNIX

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling

The difference between accuracy and recall

history key frames (history submap for loop closure)

global accuracy_list global predict_list代码意思

激光slam如何改进

用英文写一段简述Scan-to-scan matching技术在SLAM激光雷达建图方面的贡献

global accuracy_list

"""Get the accuracy and IoU for each class in the table format"""什么意思

为解决遥感图像难分类问题，本文提出一种结合 CNN 与 Transformer 优点的图像分类 方法。翻译成英文

会员权益专享

最新资源

为解决遥感图像难分类问题，本文提出一种结合 CNN 与 Transformer 优点的图像分类方法。翻译成英文