We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two sub-systems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://git.io/lvi-sam

翻译：我们提议了一个通过平滑和绘图、LVI-SAM进行紧密相交的利达-视觉-视觉-视觉-内皮测量的框架,通过光滑和绘图,实现实时国家估计和地图建设,并具有高度精确和稳健性。LVI-SAM建在一个要素图上,由两个子系统组成:视觉-内皮系统(VIS)和利达-内皮系统(LIS)。两个子系统的设计是紧密相交的,使VIS利用LIS估算来利用LIS进行方便初始化。通过利用利达尔测量为视觉特征提取深度信息,使VIS的准确性得到提高。反过来,LVI-S利用VIS估计用于初步猜测,以支持扫描-匹配。 Loop关闭首先由VIS确定,然后由LIS加以进一步完善。LVI-SAM还可以在两个子系统之一失灵时发挥作用,这提高了其在无纹理和无特征环境中的可靠性。LVI-SAMAMVI在多个平台上广泛评估了数据系统。