  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.
