Abstract
The multiple light detection and ranging (LiDAR) system has been attached to numerous autonomous vehicles to reduce blind spot risks and increase measurement resolution. It is also important to accurately aligning multi-LiDAR data because misaligned sensor data can affect the localization and perception algorithms of LiDAR sensors. However, precise extrinsic parameter estimation is a challenging research area because the feature points in LiDAR data cannot be precisely aligned owing to the LiDAR resolution. In this paper, we proposed a targetless multi-LiDAR calibration method without any initial information. In the proposed approach, a random sample consensus (RANSAC)-based plane fitting technique was utilized for plane extraction, and cross-feature points were extracted from the polar view transform of segmented planes. To resolve the discontinuity problem of optimization, a nonlinear optimization method was utilized, and the experiments were performed within a structured environment. Overall, the experimental results showed that the proposed method yields comparable results with state-of-the-art methods.
Highlights
Multiple sensor systems (MSSs) are commonly used in autonomous vehicles (AV) and mobile robots
2) We proposed a targetless extrinsic multi-light detection and ranging (LiDAR) calibration method that utilizes plane segmentation to estimate corner features without the initial values related to experimental environments
3) We proposed a method for estimating the extrinsic calibration parameters of heterogeneous 3D LiDAR sensors with different resolutions
Summary
Multiple sensor systems (MSSs) are commonly used in autonomous vehicles (AV) and mobile robots. Calibration was performed using various proposed artificial markers to estimate the extrinsic parameters of multiple sensor systems [14]. The calibration methods that are based on artificial markers are inefficient in terms of maintenance because engineers must periodically perform calibration to guarantee sensor durability. 1) This paper verified the proposed method's superiority through an extensive survey and comparative experiments performed on artificial marker-based and targetless methods for the calibration of 3D LiDARs. 2) We proposed a targetless extrinsic multi-LiDAR calibration method that utilizes plane segmentation to estimate corner features without the initial values related to experimental environments. 3) We proposed a method for estimating the extrinsic calibration parameters of heterogeneous 3D LiDAR sensors with different resolutions
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