YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems (2407.18043v1)

Abstract: In a multi-sensor fusion system composed of cameras and LiDAR, precise extrinsic calibration contributes to the system's long-term stability and accurate perception of the environment. However, methods based on extracting and registering corresponding points still face challenges in terms of automation and precision. This paper proposes a novel fully automatic extrinsic calibration method for LiDAR-camera systems that circumvents the need for corresponding point registration. In our approach, a novel algorithm to extract required LiDAR correspondence point is proposed. This method can effectively filter out irrelevant points by computing the orientation of plane point clouds and extracting points by applying distance- and density-based thresholds. We avoid the need for corresponding point registration by introducing extrinsic parameters between the LiDAR and camera into the projection of extracted points and constructing co-planar constraints. These parameters are then optimized to solve for the extrinsic. We validated our method across multiple sets of LiDAR-camera systems. In synthetic experiments, our method demonstrates superior performance compared to current calibration techniques. Real-world data experiments further confirm the precision and robustness of the proposed algorithm, with average rotation and translation calibration errors between LiDAR and camera of less than 0.05 degree and 0.015m, respectively. This method enables automatic and accurate extrinsic calibration in a single one step, emphasizing the potential of calibration algorithms beyond using corresponding point registration to enhance the automation and precision of LiDAR-camera system calibration.

• The paper introduces an automated calibration method that bypasses manual correspondence registration to accurately estimate extrinsic parameters.
• It presents a novel plane extraction technique that leverages spatial geometry to reduce data volume and streamline the calibration process.
• The optimization-based approach achieves high precision with rotation errors under 0.05° and translation errors under 0.015 m, outperforming existing methods.

Overview of "YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems"

The paper "YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems" introduces a fully automated method for the extrinsic calibration of LiDAR-camera systems. The primary focus of this research is on enhancing the automation and accuracy of the extrinsic parameter estimation process, a critical component in multi-sensor fusion systems.

Core Contributions

1. Automation Over Correspondence Registration: The paper presents a calibration approach that bypasses the traditional requirement for correspondence point registration between the LiDAR and camera. This is achieved through an innovative algorithm that extracts necessary LiDAR correspondence points, computing their orientations, and applying distance- and density-based thresholds to filter irrelevant points.
2. Novel Plane Extraction Method: The authors propose a method for extracting plane point clouds, which incorporates prior knowledge of spatial geometry. This method not only facilitates the calibration process but also significantly reduces the volume of input data required, thus addressing the time-intensive aspects commonly associated with existing methods.
3. Optimization-Based Parameter Estimation: By introducing extrinsic parameters into the projection of extracted points and constructing co-planar constraints, the method optimizes these parameters to solve for extrinsics directly, enhancing both the precision and robustness of the calibration.

Numerical Results

The paper reports robust experimental validation both in synthetic and real-world settings. The method achieves an average rotation error of less than 0.05° and a translation error of less than 0.015 meters. Such precision metrics suggest substantial improvements over existing techniques, making the method highly reliable for practical applications where device calibration is crucial.

Comparison with State of the Art

The authors juxtapose their method with current conventional approaches, highlighting its superior performance, particularly in effectively reducing manual intervention without compromising on the accuracy of calibration outcomes. By utilizing only a single frame for calibration, the method demonstrates efficiency and scalability, outperforming several fully and semi-automatic state-of-the-art calibration methods.

Implications and Future Directions

From a theoretical perspective, the research enhances the body of knowledge on multi-sensor fusion, especially concerning the integration of LiDAR and camera systems. Practically, the method offers a viable optimal calibration mechanism that can be incorporated into diverse applications in autonomous vehicles, robotics, and advanced sensing technology.

The research opens pathways for future exploration aiming at enhancing the robustness of plane extraction techniques in more complex and dynamic environments. In addition, focusing on developing calibration approaches that can adapt in real-time to varying environmental conditions could further solidify the application of YOCO in diverse fields.

In conclusion, "YOCO" provides a significant leap forward in LiDAR-camera calibration by streamlining the process into a single automatic step, reducing complexity, enhancing precision, and ultimately, paving the way for more sophisticated sensor fusion systems.