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 upon acceptance.

翻译：为了应对单一传感器的不完全限制造成的衰变,近年来,多传感器聚合战略,特别是在LiDAR-视觉-神经聚变区,引起了业界和研究界的极大兴趣。考虑到单镜照相机从某种方向和故障中容易受到环境光的影响,使系统降解成LIDAR-免疫系统,因此引入了多个照相机来扩大视觉观测,以提高系统的准确性和稳健性。此外,通过射程图像去除LiDAR的噪音,为最近的邻居搜索设定条件,并以ikd-Tree取代 kd-Tree,以提高效率。基于上述情况,我们提议建立一个高效的多视辅助LIDAR-肾脏测量系统(EMV-LIO),并评价其在开放数据集和我们定制数据集上的性能。实验表明,与LVI-SAM相比,算法有助于提高整个系统的准确性、稳健性和效率。我们将在接受后实施。