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Three Main Paradigms of Simultaneous Localization and Mapping (SLAM) Problem

机译：同时定位和制图（SLAM）问题的三种主要范例

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Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer and machine vision for automated scene commentary and explanation. The SLAM technique has been a developing research area in the robotics context during recent years. By utilizing the SLAM method robot can estimate the different positions of the robot at the distinct points of time which can indicate the trajectory of robot as well as generate a map of the environment. SLAM has unique traits which are estimating the location of robot and building a map in the various types of environment. SLAM is effective in different types of environment such as indoor, outdoor district, Air, Underwater, Underground and Space. Several approaches have been investigated to use SLAM technique in distinct environments. The purpose of this paper is to provide an accurate perceptive review of case history of SLAM relied on laser/ultrasonic sensors and camera as perception input data. In addition, we mainly focus on three paradigms of SLAM problem with all its pros and cons. In the future, use intelligent methods and some new idea will be used on visual SLAM to estimate the motion intelligent underwater robot and building a feature map of marine environment.
机译：同步定位和制图（SLAM）是计算机和机器视觉中用于自动场景注释和解释的最具挑战性的研究领域之一。近年来，SLAM技术一直是机器人技术领域中一个正在发展的研究领域。通过使用SLAM方法，机器人可以在不同的时间点估计机器人的不同位置，这可以指示机器人的轨迹并生成环境图。 SLAM具有独特的特征，可以估计机器人的位置并在各种类型的环境中构建地图。 SLAM在不同类型的环境中均有效，例如室内，室外区域，空气，水下，地下和太空。已经研究了几种在不同环境中使用SLAM技术的方法。本文的目的是对依赖于激光/超声波传感器和相机作为感知输入数据的SLAM病历提供准确的感知回顾。此外，我们主要关注SLAM问题的三种范式及其优缺点。未来，将在视觉SLAM上使用智能方法和一些新思想来估计运动智能水下机器人并构建海洋环境特征图。

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