无需标定板和视场重叠，实现多摄像头与激光雷达的同步标定。
Targetless Intrinsics and Extrinsic Calibration of Multiple LiDARs and Cameras with IMU using Continuous-Time Estimation
- 基于连续时间建模与束调整，联合优化内外参与时间偏移。
- 在纹理丰富的结构化环境中实现多传感器无误差累积标定。
- 适合自动驾驶等需高精度多传感器融合的场景使用。
精确的时空标定是多传感器融合的前提。然而，传感器通常存在异步问题，且相机与激光雷达视场无重叠，给内参与外参标定带来挑战。为此，我们提出一种基于连续时间与束调整（BA）的标定流程，可同时完成内参与外参（6自由度变换及时间偏移）的标定，无需视场重叠或标定板。首先，通过运动恢复结构（SFM）建立相机间数据关联，并进行相机内参自校准；其次，通过自适应体素地图构建实现激光雷达间数据关联，并在地图中优化外参；最后，通过激光雷达强度投影与相机图像间的特征匹配，进行联合优化以求解内参与外参。该流程适用于纹理丰富、结构化的环境，可对任意数量的相机与激光雷达进行同步标定，无需复杂的传感器同步触发机制。实验表明，该方法能在无共视与运动约束下有效实现传感器间的精准标定，且不产生误差累积。
原文摘要 · Abstract (English)
Accurate spatiotemporal calibration is a prerequisite for multisensor fusion. However, sensors are typically asynchronous, and there is no overlap between the fields of view of cameras and LiDARs, posing challenges for intrinsic and extrinsic parameter calibration. To address this, we propose a calibration pipeline based on continuous-time and bundle adjustment (BA) capable of simultaneous intrinsic and extrinsic calibration (6 DOF transformation and time offset). We do not require overlapping fields of view or any calibration board. Firstly, we establish data associations between cameras using Structure from Motion (SFM) and perform self-calibration of camera intrinsics. Then, we establish data associations between LiDARs through adaptive voxel map construction, optimizing for extrinsic calibration within the map. Finally, by matching features between the intensity projection of LiDAR maps and camera images, we conduct joint optimization for intrinsic and extrinsic parameters. This pipeline functions in texture-rich structured environments, allowing simultaneous calibration of any number of cameras and LiDARs without the need for intricate sensor synchronization triggers. Experimental results demonstrate our method's ability to fulfill co-visibility and motion constraints between sensors without accumulating errors.
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