In this paper, we propose an efficient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers significant advantages in terms of achieving real-time efficiency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable efficient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and efficacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://github.com/APRIL-ZJU/Coco-LIC.

翻译：本文提出了一种高效的连续时间LiDAR-惯性-相机里程计，利用非均匀B样条实现对LiDAR、IMU和相机测量值的紧耦合。与基于均匀B样条的连续时间方法相比，我们的非均匀B样条方法在实现实时效率和高精度方面具有显著优势。这是通过根据运动变化动态自适应地放置控制点来实现的。为了在短滑动窗口优化中高效融合异构的LiDAR-惯性-相机数据，我们利用全局LiDAR地图中的对应地图点为视觉像素分配深度，并为当前图像帧中的关联像素构建帧到地图重投影因子。这样避免了视觉像素深度优化的必要性，而后者通常需要较长的滑动窗口及众多控制点以实现连续时间轨迹估计。我们在真实世界数据集上进行了专门实验，以证明采用非均匀连续时间轨迹表示的优越性和有效性。我们的LiDAR-惯性-相机里程计系统还在传感器退化的挑战性场景和大规模场景中进行了广泛评估，并显示出与最先进方法相当或更高的精度。本文的代码库将在https://github.com/APRIL-ZJU/Coco-LIC 开源。