Sensor fusion has become a popular topic in robotics. However, conventional fusion methods encounter many difficulties, such as data representation differences, sensor variations, and extrinsic calibration. For example, the calibration methods used for LiDAR-camera fusion often require manual operation and auxiliary calibration targets. Implicit neural representations (INRs) have been developed for 3D scenes, and the volume density distribution involved in an INR unifies the scene information obtained by different types of sensors. Therefore, we propose implicit neural fusion (INF) for LiDAR and camera. INF first trains a neural density field of the target scene using LiDAR frames. Then, a separate neural color field is trained using camera images and the trained neural density field. Along with the training process, INF both estimates LiDAR poses and optimizes extrinsic parameters. Our experiments demonstrate the high accuracy and stable performance of the proposed method.

翻译：传感器融合已成为机器人领域的热门话题。然而，传统融合方法面临诸多困难，如数据表征差异、传感器变化及外参标定等。例如，用于激光雷达-相机融合的标定方法通常需要人工操作和辅助标定靶。隐式神经表示(INR)已被开发用于三维场景，其涉及的体密度分布统一了不同传感器获取的场景信息。为此，我们提出针对激光雷达与相机的隐式神经融合(INF)方法。INF首先利用激光雷达帧训练目标场景的神经密度场，随后通过相机图像与已训练的神经密度场分别训练神经颜色场。在训练过程中，INF同时估计激光雷达位姿并优化外参参数。实验证明，该方法具有高精度与稳定性能。