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Fast Extrinsic Calibration for 3D LIDAR

Published: 19 July 2019 Publication History

Abstract

3D LIDAR based environmental perception has been widely used in the field of robotics research especially for the rapid development of the vehicle's auto pilot. Coordinate transformation calibration between LIDAR and the self-driving vehicle's body is a prerequisite for environmental perception. Different from the usual LIDAR extrinsic calibration equipped with IMU, the transform matrix is calculated by using the vertical relationship between the flat ground plane and another flat plane perpendicular to the ground without depending on third-party sensors such as IMU or satellite positioning devices in the proposed method.
The calibration process is divided into two steps. The first step is to determine the transform matrix from the LIDAR's frame to the self-defined ground plane's frame. The second step is to determine the transform matrix from the ground plane's frame to the vehicle's frame. The proposed calibration method is fast and valid and has been validated on a variety of LIDAR equipment including Pandar40, VLP-16, HDL-32E and so on. The calibration result is used for 3D-LIDAR based environmental perception and map reconstruction in the process of auto pilot. The experimental results show that the proposed calibration method is reliable and convenient.

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Cited By

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  • (2020)3D-Lidar Based Negative Obstacle Detection in Unstructured EnvironmentProceedings of the 2020 4th International Conference on Vision, Image and Signal Processing10.1145/3448823.3448865(1-6)Online publication date: 9-Dec-2020

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cover image ACM Other conferences
CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering
July 2019
478 pages
ISBN:9781450371865
DOI:10.1145/3351917
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Sichuan University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2019

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Author Tags

  1. 3D LIDAR
  2. Environmental perception
  3. Extrinsic calibration
  4. Transform matrix

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  • (2020)3D-Lidar Based Negative Obstacle Detection in Unstructured EnvironmentProceedings of the 2020 4th International Conference on Vision, Image and Signal Processing10.1145/3448823.3448865(1-6)Online publication date: 9-Dec-2020

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