The recent adoption of the Robot Operating System (ROS) as a software standard in robotics has contributed to novel solutions for several problems on the area. One such problem is known as Simultaneous Localization and Mapping (SLAM) with autonomous navigation, for which a number of algorithms from different classes are available as ROS packages ready to be used on any compatible robot. Many anticipated applications of autonomous mobile robots require for them to navigate in diverse complex environments without support from exterior infrastructures. To perform this on-board navigation, the robot must make use of the available sensor technologies and fuse the most reliable data respective to the present environment in an adaptive manner and optimize the algorithm parameters prior to the actual implementation to reduce the workaround time. This paper will review recent efforts to develop onboard navigation systems which can seamlessly transition between outdoor and indoor environments and different terrains seamlessly using Gazebo simulator with ROS integration. The methodologies surveyed include SLAM, Odometry and Localisation. An overview of the state-of-the-art is provided with a focus on approaches which are adaptive to dynamic sensor uncertainty, dynamic objects and dynamic scenes. The experiences reported on this work should provide insight for roboticists seeking an Autonomous SLAM solution for indoor applications.

翻译：近年来，机器人操作系统（ROS）作为机器人领域的软件标准被广泛采用，推动了该领域多个问题的创新解决方案。其中一个典型问题是同时定位与地图构建（SLAM）结合自主导航技术——目前已有多类不同算法的ROS功能包可直接部署于兼容机器人。自主移动机器人的许多预期应用要求其在无外部基础设施支持的多样化复杂环境中执行导航任务。为实现星载导航，机器人需利用可用传感器技术，根据当前环境自适应融合最可靠数据，并在实际部署前优化算法参数以缩短调试周期。本文综述了近期基于Gazebo仿真器与ROS集成的星载导航系统研究进展，这些系统可无缝切换于室内外环境及不同地形。所调研的方法涵盖SLAM、里程计与定位技术。本文重点阐述能自适应动态传感器不确定性、动态物体和动态场景的先进方法，并概述该领域研究现状。本文所述经验将为寻求室内自主SLAM解决方案的机器人研究人员提供重要参考。