ArticleSensors (Basel, Switzerland)2024
Probability-Based LIDAR-Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- T360Fusion: Temporal 360 Multimodal Fusion for 3D Object Detection via Transformers.Sensors (Basel, Switzerland) · 2025Article
- Singular Value Decomposition (SVD) Method for LiDAR and Camera Sensor Fusion and Pattern Matching Algorithm.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR-camera's external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods.
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Registered trials
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