Abstract
Visual Odometry Implementation and Accuracy Evaluation Based on Real-time Appearance-based Mapping
Highlights
In recent years, with the rapid development of artificial intelligence and machine learning technology, various robots serving humans have emerged and are widely used in many fields, such as military, industry, agriculture, and daily life
Real-time appearance-based mapping (RTAB-MAP) is a more classic solution in RGB-D simultaneous localization and mapping (SLAM). It implements everything from feature-based visual odometry (FVO), bag-based loop detection, back-end pose map optimization, and point cloud and triangle mesh map generation
We conducted an in-depth research on the implementation and accuracy evaluation of visual odometry (VO) based on RTAB-MAP
Summary
With the rapid development of artificial intelligence and machine learning technology, various robots serving humans have emerged and are widely used in many fields, such as military, industry, agriculture, and daily life. Achieving the autonomous behavior and decision-making of mobile robots has always been one of the research hotspots, and the positioning and navigation of robots have become indispensable. Traditional robot positioning often uses the inertial navigation system (INS) combined with the global positioning system (GPS), wheel odometer or other methods. INS-based positioning has the characteristics of high frequency and short-time precision, but its error increases with time. GPS is the most mature global positioning technology available today. In ISSN 0914-4935 © MYU K.K. https://myukk.org/
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