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- W3003399386 abstract "Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras, and lidars by providing appropriate transformational relationships between the corresponding reference frames. For wheeled robots equipped with exteroceptive sensors, calibration entails learning the motion model of the sensor or the robot in terms of the odometric data, and must generally be performed prior to performing tasks such as simultaneous localization and mapping (SLAM). Within this context, the current trend is to carry out simultaneous calibration of odometry and sensor without the use of any additional hardware. Building upon the existing simultaneous calibration algorithms, we put forth a robust joint calibration methodology using scan matching framework that can handle outliers due to non-systematic errors in an automated manner. Leveraging the alternating minimization technique, the proposed approach not only has superior performance as compared to the state-of-the-art methods but also does not require any manual trimming of outliers. Detailed experiments are performed to demonstrate the accuracy, usefulness, and flexibility of the proposed scheme." @default.
- W3003399386 created "2020-02-07" @default.
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- W3003399386 date "2019-07-02" @default.
- W3003399386 modified "2023-09-26" @default.
- W3003399386 title "Automatic Joint Calibration of odometry and sensor parameters" @default.
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- W3003399386 doi "https://doi.org/10.1145/3352593.3352659" @default.
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