To deal with the degeneration caused by the incomplete constraints of single sensor, multi-sensor fusion strategies especially in LiDAR-vision-inertial fusion area have attracted much interest from both the industry and the research community in recent years. Considering that a monocular camera is vulnerable to the influence of ambient light from a certain direction and fails, which makes the system degrade into a LiDAR-inertial system, multiple cameras are introduced to expand the visual observation so as to improve the accuracy and robustness of the system. Besides, removing LiDAR's noise via range image, setting condition for nearest neighbor search, and replacing kd-Tree with ikd-Tree are also introduced to enhance the efficiency. Based on the above, we propose an Efficient Multiple vision aided LiDAR-inertial odometry system (EMV-LIO), and evaluate its performance on both open datasets and our custom datasets. Experiments show that the algorithm is helpful to improve the accuracy, robustness and efficiency of the whole system compared with LVI-SAM. Our implementation will be available at https://github.com/thinking-08/EMV-LIO.git.

翻译：针对单一传感器约束不完整导致的退化问题，多传感器融合策略（尤其是激光雷达-视觉-惯性融合领域）近年来引起了工业界和学术界的广泛关注。考虑到单目相机易受特定方向环境光影响而失效，导致系统退化为激光雷达-惯性系统，本文引入多台相机以扩展视觉观测范围，从而提升系统的精度和鲁棒性。此外，通过距离图像去除激光雷达噪声、设置最近邻搜索条件、以及用ikd-Tree替代kd-Tree等方法，进一步提高了系统的效率。基于上述工作，我们提出了一种高效的多视觉辅助激光雷达-惯性里程计系统（EMV-LIO），并在公开数据集和自建数据集上评估其性能。实验表明，与LVI-SAM相比，该算法有助于提升整个系统的精度、鲁棒性和效率。我们的实现代码将在https://github.com/thinking-08/EMV-LIO.git上公开。