A Tutorial: Mobile Robotics, Slam, Bayesian Filter, Keyframe Bundle Adjustment and Ros Applications

Abstract

Autonomous mobile robots, an important research topic today, are often developed for smart industrial environments where they interact with humans. For autonomous movement of a mobile robot in an unknown environment, mobile robots must solve three main problems; localization, mapping and path planning. Robust path planning depends on successful localization and mapping. Both problems can be overcome with Simultaneous Localization and Mapping (SLAM) techniques. Since sequential sensor information is required for SLAM, eliminating these sensor noises is crucial for the next measurement and prediction. Recursive Bayesian filter is a statistical method used for sequential state prediction. Therefore, it is an essential method for the autonomous mobile robots and SLAM techniques. This study deals with the relationship between SLAM and Bayes methods for autonomous robots. Additionally, keyframe Bundle Adjustment (BA) based SLAM, which includes state-of-art methods, is also investigated. SLAM is an active research area and new algorithms are constantly being developed to increase accuracy rates, so new researchers need to understand this issue with ease. This study is a detailed and easily understandable resource for new SLAM researchers. ROS (Robot Operating System)-based SLAM applications are also given for better understanding. In this way, the reader obtains the theoretical basis and application experience to develop alternative methods related to SLAM. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.