Motion-based Calibration between Multiple LiDARs and INS with Rigid Body Constraint on Vehicle Platform

Kim, Hyungjin; Rangan, Sathya Narayanan Kasturi; Pagad, Shishir; Yalla, Veera Ganesh

doi:10.1109/iv47402.2020.9304532

Cited by 5 publications

(4 citation statements)

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“…The graph-optimization-based approach was also proposed to consider properties for sensors mounted in an autonomous vehicle [ 13 ]. By applying additional constraints, it improved robustness and reliability.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Several studies evaluate their calibration parameter by errors between estimated ones and ground truth that are given by either the KITTI dataset or their own dataset [ 7 , 13 , 15 ]. With ground truth, a mean and standard deviation of errors can represent the reference best fitness of the system.…”

Section: Previous Studiesmentioning

confidence: 99%

“…For real data, it is difficult to find ground-truth data with simulation. In this case, the standard deviation of the parameters, convergence, or repeatability can be used to evaluate how the proposed system is robust and stable [ 13 , 16 , 17 , 18 ]. The mean of parameters are useful to figure out the precision of the system but cannot evaluate accuracy from the biased results.…”

Section: Previous Studiesmentioning

confidence: 99%

“…The mean of parameters are useful to figure out the precision of the system but cannot evaluate accuracy from the biased results. A performance index based on map smoothness that is related to mapping performance was applied to evaluate parameters [ 13 , 19 , 20 , 21 ]. Map smoothness is introduced as a metric that can be used to determine how blurry a map will be generated through the estimated calibration parameter.…”

Section: Previous Studiesmentioning

confidence: 99%

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Performance Index for Extrinsic Calibration of LiDAR and Motion Sensor for Mapping and Localization

Kim

2021

Sensors

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Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization.

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Section: Previous Studiesmentioning

confidence: 99%