Automatic Calibration of Multiple LIDAR Sensors Using a Moving Sphere as Target

Pereira, Marcelo Silva; Santos, Vítor; Dias, Paulo

doi:10.1007/978-3-319-27146-0_37

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Giving continuity to the work presented in [19], the integration of the cameras on the calibration process was also addressed, initially with a non-uniform color ball (see Fig. 12), which complicated its detection, therefore the ball was replaced by a uniform color (red) ball.…”

Section: Ball Detection On Camerasmentioning

confidence: 99%

Self calibration of multiple LIDARs and cameras on autonomous vehicles

Pereira

Schwartz

Santos

et al. 2016

Robotics and Autonomous Systems

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Section: Ball Detection On Camerasmentioning

confidence: 99%

Self calibration of multiple LIDARs and cameras on autonomous vehicles

Pereira

Schwartz

Santos

et al. 2016

Robotics and Autonomous Systems

Self Cite

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“…The method based on calibration equipment refers to the estimation of spatial transformation parameters using prior information provided by external sensors, rotary tables, pendulums, calibration targets, and other equipment. The calibration accuracy of this method is related to the calibration equipment [ 5 , 6 , 7 , 8 ]. For example, Liu et al used a precision turntable, a standard plane, and spherical and cylindrical targets to estimate the motion of LiDAR, modeled the calibration problem as a maximal likelihood estimation problem, and solved it, but this method is time-consuming and labor-intensive, and requires expensive calibration equipment.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Calibration Algorithm for IMU–LiDAR Navigation System Based on Similarity of Motion Trajectories

Yang

Xiu

et al. 2022

Sensors

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The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems.

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