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- W3128119518 abstract "Simultaneous localization and mapping (SLAM) algorithms aimed for autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs." @default.
- W3128119518 created "2021-02-15" @default.
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- W3128119518 date "2021-01-10" @default.
- W3128119518 modified "2023-10-16" @default.
- W3128119518 title "AV-SLAM: Autonomous Vehicle SLAM with Gravity Direction Initialization" @default.
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- W3128119518 doi "https://doi.org/10.1109/icpr48806.2021.9412466" @default.
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