A Camera to LiDAR calibration approach through the optimization of atomic transformations

Aguiar, André Silva Pinto de; Oliveira, Miguel; Pedrosa, Eurico; Santos, Filipe

doi:10.1016/j.eswa.2021.114894

Cited by 12 publications

(4 citation statements)

References 42 publications

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represents the extrinsic parameters between the two sensors, consisting of a rotation matrix and a translation matrix. These extrinsic parameters, which indicate the relative position and direction between the two sensors, can be obtained through calibration [ 22 ].

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Section: Preliminariesmentioning

confidence: 99%

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation

Jung,

Jee

2024

Sensors

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In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

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“…

…”

Section: Preliminariesmentioning

confidence: 99%

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation

Jung,

Jee

2024

Sensors

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“…More recently, [ 8 ] presents a simultaneous general approach for hand–eye calibration for multiple cameras, based on optimization of atomic transformations dubbed ATOM. In [ 9 ], the authors build upon ATOM to allow multimodal sensors, but the method still necessitates a camera due to reprojection error being used as optimization metric. Both calibration methods are target-based and the sensors must share field of view.…”

Section: Related Workmentioning

confidence: 99%

Motion-Based Extrinsic Sensor-to-Sensor Calibration: Effect of Reference Frame Selection for New and Existing Methods

Välimäki

Garigipati

Ghabcheloo

2023

Sensors

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This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand–eye calibration problem. As the sensor trajectories typically contain some composition of noise, the aim is to determine which selection strategies work best under which noise conditions. Different reference selection options are tested under varying noise conditions in simulations, and the findings are validated with real data from the KITTI dataset. The study is conducted for four state-of-the-art methods, as well as two proposed cost functions for nonlinear optimization. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in the literature, is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.

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“…More recently, [8] presents a simultaneous general approach for handeye calibration for multiple cameras, based on optimization of atomic transformations dubbed ATOM. In [9], the authors build upon ATOM to allow multimodal sensors, but the method still necessitates a camera due to reprojection error being used as optimization metric. Both calibration methods are target based and the sensors must share field of view.…”

Section: Related Workmentioning

confidence: 99%

Motion-based extrinsic sensor-to-sensor calibration: Effect of reference frame selection for new and existing methods

Vӓlimӓki¹,

Garigipati²,

Ghabcheloo³

2023

Preprint

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This paper studies the effect of reference frame selection in sensor-to-sensor extrinsic calibration when formulated as a motion-based hand-eye calibration problem. Different reference selection options are tested under varying noise conditions in simulation, and the findings are validated with real data from the KITTI dataset. We propose two nonlinear cost functions for optimization and compare them with four state-of-the-art methods. One of the proposed cost functions incorporates outlier rejection to improve calibration performance and was shown to significantly improve performance in the presence of outliers, and either match or outperform the other algorithms in other noise conditions. However, the performance gain from reference frame selection was deemed larger than that from algorithm selection. In addition, we show that with realistic noise, the reference frame selection method commonly used in literature is inferior to other tested options, and that relative error metrics are not reliable for telling which method achieves best calibration performance.

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A Camera to LiDAR calibration approach through the optimization of atomic transformations

Cited by 12 publications

References 42 publications

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation

Motion-Based Extrinsic Sensor-to-Sensor Calibration: Effect of Reference Frame Selection for New and Existing Methods

Motion-based extrinsic sensor-to-sensor calibration: Effect of reference frame selection for new and existing methods

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