Simultaneous localization and mapping (SLAM) of an unknown environment is a primary requirement for any autonomous robot. In this paper we present a cost-effective, robust, and integrated software/hardware SLAM solution for 2D mapping of large indoor environments. The presented SLAM solution consists of a robotic platform, referred as MapperBot, and an effective implementation of SLAM algorithm, referred as iSCAN. MapperBot is built using commercial of-the-shelf components with an open-source software/hardware design presented in this paper. The SLAM algorithm iSCAN is based on a highly robust laser-scan matching technique, incorporating Rao-Blackwellized particle filter as a mean to increase its accuracy. As a result, a minimal number of particles is required, and the computer processing time is dramatically reduced. MapperBot/iSCAN solution is fully integrated with the Matlab environment, thus providing researchers, students and developers a quick entry into the realm of the SLAM.

中文翻译：

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MapperBot / iSCAN：用于2D映射的开源集成机器人平台和算法

未知环境的同时定位和映射（SLAM）是任何自主机器人的主要要求。在本文中，我们提出了一种用于大型室内环境的2D映射的经济高效，强大且集成的软件/硬件SLAM解决方案。提出的SLAM解决方案包括一个称为MapperBot的机器人平台，以及一个称为iSCAN的SLAM算法的有效实现。MapperBot使用现成的商用组件构建，并具有本文介绍的开源软件/硬件设计。SLAM算法iSCAN基于高度鲁棒的激光扫描匹配技术，并结合了Rao-Blackwellized粒子滤波器来提高其准确性。结果，需要最少数量的颗粒，并且大大减少了计算机处理时间。