A New Flying Range Sensor: Aerial Scan in Omni-Directions

Zheng, Bo; Huang, Xiangqi; Ishikawa, Ryoichi; Oishi, Takeshi; Ikeuchi, Katsushi

doi:10.1109/3dv.2015.77

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…Then the sensors are moved around to obtain images of each chessboard to reconstruct the visual 3D point cloud. Note that this differs from previous approaches [8], [9] which require multiple images and the LiDAR data of a single chessboard presented at different poses as inputs; the hidden limitation of these methods is that a common field of view between sensors is needed.…”

Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 85%

“…Numerous efforts have been carried out to perform Li-DARcamera extrinsic calibration [8], [9], [10]. The current calibration approaches can be classified into two groups [11]: one is appearance-based and the other is motion-based.…”

Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 99%

“…The error-state transition matrix is the same as the standard one. Turning to the measurement Jacobian matrix, besides the feature reprojection measurement error h proj , there is point-to-plane measurement error h pl in our system according to (8):…”

Section: B Observability Of Our Calibration Systemmentioning

confidence: 99%

“…We conducted real-world experiments and sufficiently compared the three different calibration methods: the KITTI single shot calibration method [14]; MO methods: multiple images and LiDAR data of a single calibration target presented in different directions as input which is fussy due to the need to move the chessboard, similar to [8], [9]; and the motionbased calibration method: based on trajectory alignment.…”

Section: B Real-world Experimentsmentioning

confidence: 99%

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LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

Wang

Ding

et al. 2020

IEEE Trans. Instrum. Meas.

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LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a novel LiDAR-camera calibration method aiming to eliminate these two constraints. Specifically, we capture a scan of 3D LiDAR when both the environment and the sensors are stationary, then move the camera to reconstruct the 3D environment using the sequentially obtained images. Finally, we align 3D visual points to the laser scan based on tightly couple graph optimization method to calculate the extrinsic parameters between LiDAR and camera. Under this design, the configuration of these two sensors are free from the common field of view constraint owing to the extended view from the moving camera. And we also eliminate the requirement for strict time synchronization as we only use the single scan of laser data when the sensors are stationary. We theoretically derive the conditions of minimal observability for our method and prove that the accuracy of calibration is improved by collecting more observations from multiple scattered calibration targets. We validate our method on both simulation platform and real-world datasets. Experiments show that our method achieves higher accuracy than other comparable methods, which is in accordance with our theoretical analysis. In addition, the proposed method is beneficial to not only plane measurement error based chessboard, but also other point measurement error based calibration targets, such as boxes and polygonal boards.

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Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 85%

Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 99%

Section: B Observability Of Our Calibration Systemmentioning

confidence: 99%

Section: B Real-world Experimentsmentioning

confidence: 99%

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LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

Wang

Ding

et al. 2020

IEEE Trans. Instrum. Meas.

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“…For example, by combining cameras with LiDAR, it is possible to perform color mapping on range images ( Fig. 1) or estimate accurate sensor motion for mobile sensing systems [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry

Ishikawa

Oishi

Ikeuchi

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

120

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In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration. Our approach is an extension of hand-eye calibration framework to 2D-3D calibration. By using the sensor fusion odometry method, the scaled camera motions are calculated with high accuracy.In addition to this, we clarify the suitable motion for this calibration method.The proposed method only requires the three-dimensional point cloud and the camera image and does not need other information such as reflectance of LiDAR and to give initial extrinsic parameter. In the experiments, we demonstrate our method using several sensor configurations in indoor and outdoor scenes to verify the effectiveness. The accuracy of our method achieves more than other comparable state-of-the-art methods.

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Time Synchronization and Space Registration of Roadside LiDAR and Camera

Wang¹,

Liu

Wang³

et al. 2023

Electronics

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The sensing system consisting of Light Detection and Ranging (LiDAR) and a camera provides complementary information about the surrounding environment. To take full advantage of multi-source data provided by different sensors, an accurate fusion of multi-source sensor information is needed. Time synchronization and space registration are the key technologies that affect the fusion accuracy of multi-source sensors. Due to the difference in data acquisition frequency and deviation in startup time between LiDAR and the camera, asynchronous data acquisition between LiDAR and camera is easy to occur, which has a significant influence on subsequent data fusion. Therefore, a time synchronization method of multi-source sensors based on frequency self-matching is developed in this paper. Without changing the sensor frequency, the sensor data are processed to obtain the same number of data frames and set the same ID number, so that the LiDAR and camera data correspond one by one. Finally, data frames are merged into new data packets to realize time synchronization between LiDAR and camera. Based on time synchronization, to achieve spatial synchronization, a nonlinear optimization algorithm of joint calibration parameters is used, which can effectively reduce the reprojection error in the process of sensor spatial registration. The accuracy of the proposed time synchronization method is 99.86% and the space registration accuracy is 99.79%, which is better than the calibration method of the Matlab calibration toolbox.

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A New Flying Range Sensor: Aerial Scan in Omni-Directions

Cited by 3 publications

References 22 publications

LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry

Time Synchronization and Space Registration of Roadside LiDAR and Camera

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