论文标题
SOIC：LIDAR和相机的语义在线初始化和校准
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera
论文作者
论文摘要
本文介绍了一种新型的基于语义的在线外部校准方法SOIC（SOIC（SO，我看到），以进行光检测和范围（LIDAR）和相机传感器。以前的在线校准方法通常需要对粗略初始值进行优化的先验知识。提出的方法通过引入语义质心（SCS）将初始化问题转换为透视N点（PNP）问题来消除此限制。该PNP问题的封闭式解决方案已经进行了充分的研究，可以通过现有的PNP方法找到。由于点云的语义质心通常与相应图像的语义质量不准确匹配，因此即使在非线性细化过程之后，参数的精度也不会提高。因此，制定了基于点云和图像数据的语义元素之间对应关系的限制的成本函数。随后，通过最小化成本函数来估计最佳外部参数。我们在Kitti数据集上使用GT或预测语义评估了所提出的方法。实验结果和与基线方法的比较验证了初始化策略的可行性以及校准方法的准确性。此外，我们在https://github.com/-/soic上发布源代码。
This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors. Previous online calibration methods usually need prior knowledge of rough initial values for optimization. The proposed approach removes this limitation by converting the initialization problem to a Perspective-n-Point (PnP) problem with the introduction of semantic centroids (SCs). The closed-form solution of this PnP problem has been well researched and can be found with existing PnP methods. Since the semantic centroid of the point cloud usually does not accurately match with that of the corresponding image, the accuracy of parameters are not improved even after a nonlinear refinement process. Thus, a cost function based on the constraint of the correspondence between semantic elements from both point cloud and image data is formulated. Subsequently, optimal extrinsic parameters are estimated by minimizing the cost function. We evaluate the proposed method either with GT or predicted semantics on KITTI dataset. Experimental results and comparisons with the baseline method verify the feasibility of the initialization strategy and the accuracy of the calibration approach. In addition, we release the source code at https://github.com/--/SOIC.
