SCI每部分必备例句

1.abstract

Map generation by a robot in a cluttered and noisy environment
is an important problem in autonomous robot navigation.

Localization, mapping, remote operation, maintenance, and
health and safety are identified as the main beneficiaries
from rapidly developing technologies, such as 3-D visualization,
augmented reality, energy autonomous sensor nodes,
distributed sensing, smart network protocols, and big data analytics.

mobile robot automation relies heavily
on technologies such as sensing, localization, mapping.

摘要第一句和第二句：
1.背景
Robust localization and mapping by a robot in a cluttered and noisy indoor environment
is an important problem in autonomous robot navigation.

Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method
to construct an indoor map.
2.目的第三句
1.The proposed solution exploits…
2.This paper proposes a … method based on…
3.In this paper we propose a method for …
4.This paper proposes an SLAM algorithm based on …
5.This paper presents an … algorithms applied to … for indoor environments
6.This paper therefore proposes …
7.This paper, however, proposes …
8.This paper further proposes a new method to
This paper proposes a SlAM algorithm based on Monte-Carlo Localization and Mapping
methods with discrete hough transform in order to build a robust robot navigation system.
3.方法第四句
1.This article is an attempt to…
2.
A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans.
The set of relative rotation angles and corresponding spatial displacements of the robot are estimated according to Hough spatial energy spectrum correlation functions construction of global and local 2D Occupancy Grid Maps,
Further more,to use the result serve as the guiding particle set of the Monte Carlo global location algorithm.
4.结果概述第五局
1.Several systematic experiments for evaluating the approach have been performed both
on a simulator and on soccer robots embedded in the RoboCup environment.
2.Experimental results are presented to illustrate the performance of the proposed algorithm.
3.We also present empirical results from experimental evaluations of our approach
in real world scenarios both from the perspective of the navigation capability and the usability of the interface.
5.Several systematic experiments for evaluating the proposed algorithm for mapping and localization
have been performed in both simulated and real world scenarios,
and found it efficiency and build maps with good self-localization.

Introduction
第一部分：研究背景及其重要性（有大到小，有古道今）
In the past 30 years, plenty of scan matching methods have been developed.

It is important for a mobile robot to be able to autonomously navigate and localize 

itself in a known or unknown environment. A precise position estimation always serves as
the heart in any navigation systems, such as localization,map building or path planning.

One primary issue is how to accurately match sensed data against information in a priori map or information that has

been continuously collected. There are two common matching techniques that havebeen used in mobile robotics: point-based
matching and feature-based matching.
Simultaneous localization and mapping (SLAM) in unknown GPS-denied environments is a major challenge
for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist;

Autonomous navigation by a mobile robot in an unknown

environment is an important problem in mobile robotics.
The robot mainly perceives the outer world by utilizing
measurement sensors attached to it. These sensors (typi‐
cally laser range finders, ultrasonic sensors, cameras and
3D sensors) give an estimate of the robot‘s position in the
environment and incrementally build the map of the
environment. This problem is often referred to as ’simulta‐
neous localization and mapping’ (SLAM), in which a robot
simultaneously localizes its position in the environment
and builds a map of it. Hence, it becomes important for the
robot to have an accurate map. While sensors such as wheel
encoders measure the relative robot displacement, external
sensor data can be used to correlate subsequent robot
positions to get the relative pose or estimate, improving
upon the odometry data. However, in reality, sensors are
prone to errors and generate noise in the data. This noise
then accumulates as error over time and gives the wrong
estimate or else the wrong map. The environments in which
the robot navigates are mostly dynamic, which adds to the
complexity of the mapping. The main task of the robot is to
incrementally build the map of the workspace as it discov‐
ers the environment, and to avoid obstacles, in order to
reach its final goal. Various approaches to solving this
problem have been discussed by researchers

Localization and navigation are essential problems in 

mobile robotics, which have been studied by many researchers
in this area [1-3]. Moreover, the accuracy of localization have
a direct influence on the accuracy of navigation, so a mobile
robot which can perform assigned task quickly and rightly is
based on high accuracy of localization.

Localization is a well s