• 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.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.129-140
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• 2023

Autonomous driving systems are likely to be operated in various complex environments. However, the well-known integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), which is currently the major source for absolute position information, still has difficulties in accurate positioning in harsh signal environments such as urban canyons. To overcome these difficulties, integrated Visual/Inertial/GNSS (VIG) navigation systems have been extensively studied in various areas. Recently, a Compressed-State Constraint Kalman Filter (CSCKF)-based VIG navigation system (CSCKF-VIG) using a monocular camera, an Inertial Measurement Unit (IMU), and GNSS receivers has been studied with the aim of providing robust and accurate position information in urban areas. For this new filter-based navigation system, on the basis of time-propagation measurement fusion theory, unnecessary camera states are not required in the system state. This paper presents a performance evaluation of the CSCKF-VIG system compared to other conventional navigation systems. First, the CSCKF-VIG is introduced in detail compared to the well-known Multi-State Constraint Kalman Filter (MSCKF). The CSCKF-VIG system is then evaluated by a field experiment in different GNSS availability situations. The results show that accuracy is improved in the GNSS-degraded environment compared to that of the conventional systems.