Time-efficient intrinsic calibration of an automotive lidar sensor using a tabletop setup to reach subpixel precision

Pascal E. Blessing; Jens Hofmann

doi:10.1117/12.2680272

19 October 2023 Time-efficient intrinsic calibration of an automotive lidar sensor using a tabletop setup to reach subpixel precision

Pascal E. Blessing, Jens Hofmann

Author Affiliations +

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 1273307 (2023) https://doi.org/10.1117/12.2680272
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Light detection and ranging (lidar) sensors are essential for state-of-the-art 3D perception for automated driving applications, as recent developments in the field have shown. To mitigate the risk of an unreliable object localization due to a distorted point cloud, high-precision intrinsic calibration is an important prerequisite to produce lidar sensors of high reliability. For large-scale series production, the factory calibration setup is required to be both space- and time-efficient. In this paper, we present a method for angular calibration that employs a two-dimensional calibration pattern as the core of our tabletop setup. To accelerate the calibration procedure, we perform a continuous measurement of the entire field of view without accumulation over several images or sub-resolution sampling. In our evaluation, we utilize two different calibration patterns, where we extract their center point using image processing techniques. The parameter describing the precision, is the standard deviation of the pattern’s center point over a sequence of images. This is the key criterion for determining the overall measurement uncertainty of our method and selecting the optimal pattern to realize a time-efficient intrinsic calibration on the subpixel level.

Conference Presentation

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Pascal E. Blessing and Jens Hofmann "Time-efficient intrinsic calibration of an automotive lidar sensor using a tabletop setup to reach subpixel precision", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 1273307 (19 October 2023); https://doi.org/10.1117/12.2680272

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