EMV-LIO：一种高效的多视觉辅助激光雷达-惯性里程计
EMV-LIO: An Efficient Multiple Vision aided LiDAR-Inertial Odometry
AI总结：
本文提出EMV-LIO系统，通过多相机扩展视觉观测并结合距离图像去噪、最近邻搜索条件设置及ikd-Tree替换kd-Tree等优化，在公开与自建数据集上验证了其相比LVI-SAM在精度、鲁棒性和效率上的提升。
摘要：
为应对单一传感器约束不完整所导致的退化问题，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研究界的广泛关注。考虑到单目相机容易受到来自特定方向的环境光影响而失效，从而使系统退化为激光雷达-惯性系统，引入多个相机来扩展视觉观测，以提高系统的精度和鲁棒性。此外，还引入了通过距离图像去除激光雷达噪声、为最近邻搜索设置条件以及用ikd-Tree替换kd-Tree等方法来提升效率。基于上述内容，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，该算法有助于提高整个系统的精度、鲁棒性和效率。我们的实现将在论文被接收后公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
