Hybrid iteration and optimization-based three-dimensional reconstruction for space non-cooperative targets with monocular vision and sparse lidar fusion

Zhang, Chi; Guo, Yonghua; Meng, Deshan; Zhu, Wei; Li, Wenjie; Peng, Jiangde; Liang, Bin

doi:10.1016/j.ast.2023.108484

Cited by 6 publications

(1 citation statement)

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“…In the field of target detection and tracking, by obtaining the extrinsic parameters between the camera and LiDAR sensor, the data from the two can be aligned in the same coordinate system to achieve multi-sensor fusion target detection and tracking tasks, thereby improving tracking accuracy. In the field of 3D reconstruction [15][16][17], obtaining an accurate spatial relationship between the two is conducive to obtaining richer and more accurate 3D scene information. Combining the high-precision 3D information from the LiDAR sensor and the RGB information from the camera ensures more efficient and reliable 3D reconstruction results.…”

Section: Introductionmentioning

confidence: 99%

Automatic Targetless Monocular Camera and LiDAR External Parameter Calibration Method for Mobile Robots

Yu,

Fan,

et al. 2023

Remote Sensing

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With the continuous development and popularization of sensor-fusion technology for mobile robots, the application of camera and light detection and ranging (LiDAR) fusion perception has become particularly important. Moreover, the calibration of extrinsic parameters between the camera and LiDAR is a crucial prerequisite for fusion. Although traditional target-based calibration methods have been widely adopted, their cumbersome operation and high costs necessitate the development of more efficient and flexible calibration methods. To address this problem, this study proposed a two-stage calibration method based on motion and edge matching. In the first stage, the preliminary estimation of the extrinsic parameters between the camera and LiDAR was performed by matching visual odometry and LiDAR odometry using a hand–eye target method. In the second stage, the calibration results from the first stage were further refined by matching the image edges and discontinuous depth point clouds. The calibration system was then tested in both simulated and actual environments. The experimental results showed that this method, which did not require specially structured targets, could achieve highly flexible and robust automated calibration. Compared to other advanced methods, the accuracy of the proposed method was higher.

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Section: Introductionmentioning

confidence: 99%