• Journal of Positioning, Navigation, and Timing
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• v.13 no.4
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• pp.379-387
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• 2024

Real-time localization is essential for autonomous driving of robots. This paper presents the implementation and a performance analysis of a localization algorithm. To estimate the position and attitude of a robot, a visual inertial odometry (VIO) algorithm based on a multi-state constraint Kalman filter is used. The sensors employed in this study are a stereo camera and an inertial measurement unit (IMU). The performance is analyzed through experiments using three different camera view directions: floor-view, front-view, and ceiling-view. The number of detected features also affects navigation performance. Even if the number of recognized feature points is large, performance degrades if the correspondence between feature points is not accurately identified. The results show that VIO improves navigation performance even with low-cost sensors, thus facilitating map building as well as autonomous navigation.