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Introduction to 3D SLAM with RTAB-Map | by Shiva Chandrachary | Medium

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RTAB-Mapping, short for Real-Time Appearance-Based Mapping, is a graph-based SLAM approach. Appearance-based SLAM means that the algorithm uses data collected from vision sensors to localize the robot and map the environment. A process called loop closures is used to determine whether the robot has seen a location before. As the robot travels to new areas in its environment, the map is expanded, and the number of images that each new image must be compared to increases. This causes the loop closures to take longer but with complexity increasing linearly. RTAB-Map is optimized for large-scale and long-term SLAM by using multiple strategies to allow for loop closure to be done in real-time. The loop closure is happening fast enough that the result can be obtained before the next camera images are acquired. Before diving deep into the RTAB-Mapping, it is quite important to understand the basics of GraphSLAM such as, what is a graph, how is one constructed, how to represent the poses and f

Introduction to 3D SLAM with RTAB-Map Shiva Chandrachary 8 min read · Jan 13, 2021 -- 3 Listen Share RTAB-Mapping, short for Real-Time Appearance-Based Mapping, is a graph-based SLAM approach. Appearance-based SLAM means that the algorithm uses data collected from vision sensors to localize the robot and map the environment. A process called loop closures is used to determine whether the robot has seen a location before. As the robot travels to new areas in its environment, the map is expanded, and the number of images that each new image must be compared to increases. This causes the loop clo

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