论文摘要写作（来自维基百科）

来自维基百科https://www.wikihow.com/Write-an-Abstract

一、摘要写作

1.基本注意事项

先写文章，后写摘要，摘要是一个文章的综述，包括背景，方法，结果，提高部分。
注意要提交到的期刊或者会议有没有对摘要有特殊要求，如风格样式，长度大小等等。
考虑你的读者，作为一个读者首先关心的是这篇文章是不是自己感兴趣的，让读者快速决定是不是他们关心的领域。
摘要写出文章的目标，方法，结果

2.开始写摘要

（1）第一步:明确研究目的

你为什么做这个研究
怎样进行你的研究
你发现了什么
你的研究为什么重要
为什么有人要看整篇文章

（2）解释问题。考虑一下你的文章的解决的特殊问题，你的动机。

你试图更好的理解或研究什么问题
研究范围是什么，通用的问题还是特殊的问题
主要解决的是什么问题

（3）解释方法。给出你的研究的概述，如果是自己的工作写一个叙述。

讨论你研究的价值和方法
描述支撑你自己观点的证据
给出最重要点的概述

（4）给出结果。

从你的研究中得到什么结果
结果是否支持自己的观点或者假设
一般的结果是什么

（5）给出结论

3.格式化摘要

（1）注意写作顺序，Introduction，Body，Conclusion

（2）只提供有用的信息，尽量不要涉及缩写，不要有图表

（3）摘要独立于正文，不要抄写正文内容，或者从其他内容摘抄过来

（4）有的期刊论文需要5-10个关键词用于检索

（5）摘要中要写真实的信息，不要包含正文中没有用到的方法，也不要写别人的idea或者conclusion。

（6）用词不要太特殊，这会使读者搞不懂。

（7）写完检查一遍，不要有语法错误。

（8）让一个完全不了解你这个project的人读一遍，告诉你他没有理解的地方。

4.SLAM领域几篇经典paper摘要分析

ORBSLAM2

Abstract—We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. （解决的问题）Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. （解释方法）The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. （给出结论）We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

VINS-MONO

Abstract—One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which
is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation.

（写作背景+明确研究目的，背景就是本文要针对六自由度状态估计问题，单目+IMU使用）

In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. （提出主要解决的问题）Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. （概述使用的方法，这也是最重要点的方法）A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization.

（解释研究的问题和方法）

We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms.（实验结果） We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization.）（这块SLAM领域基本上都要有的，和其他SORT算法在公开数据集上进行对比） We open source our implementations for both PCs (https://github.com/HKUST-Aerial-Robotics/VINS-Mono) and iOS mobile devices (https://github.com/HKUST-Aerial-Robotics/VINS-Mobile).

（给出结果）

Index Terms—Monocular visual-inertial systems (VINSs), state estimation, sensor fusion, simultaneous localization and mapping.

DSO

We propose a novel direct sparse visual odometry formulation. （省略了背景介绍，直接提出介绍本文主旨）It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry – represented as inverse depth in a reference frame – and camera motion. （介绍主要方法）This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. （结果）Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on mostly white walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. （解释方法）We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.（最后的总结）

Probabilistic Data Association for Semantic SLAM

Abstract— Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment.（解决的问题1） Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. （问题2）（问题背景，说明为什么研究这个语义SLAM问题）On the other hand, object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks, ideal for view-independent unambiguous loop closure. In a map with several objects of the same class, however, a crucial data association problem exists. While data association and recognition are discrete problems usually solved using discrete inference, classical SLAM is a continuous optimization over metric information.

（到这里都是说背景，为什么开展此项工作，解释一下问题）

In this paper, we formulate an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decompose it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states. The estimated landmark and robot poses affect the association and class distributions, which in turn affect the robot-landmark pose optimization. The performance of our algorithm is demonstrated on indoor and outdoor datasets.

5.常用句式

presents/shows/studies/discusses/proposes

The experiments show that the presented approach significantly outperforms state-of-the-art...

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