We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odometry solution is used to estimate the bias of the IMU. To ensure high performance in real-time, we marginalize old lidar scans for pose optimization, rather than matching lidar scans to a global map. Scan-matching at a local scale instead of a global scale significantly improves the real-time performance of the system, as does the selective introduction of keyframes, and an efficient sliding window approach that registers a new keyframe to a fixed-size set of prior ``sub-keyframes.'' The proposed method is extensively evaluated on datasets gathered from three platforms over various scales and environments.

中文翻译：

---

LIO-SAM：通过平滑和映射紧密耦合的激光雷达惯性里程计

我们提出了一个通过平滑和映射的紧密耦合激光雷达惯性测距框架 LIO-SAM，该框架实现了高度准确、实时的移动机器人轨迹估计和地图构建。LIO-SAM 在因子图的顶部制定激光雷达惯性里程计，允许将来自不同来源的大量相对和绝对测量值（包括闭环）作为因子纳入系统。来自惯性测量单元 (IMU) 预集成的估计运动消除了点云的偏斜，并为激光雷达测距优化产生了初始猜测。获得的激光雷达里程计解用于估计 IMU 的偏差。为了确保实时的高性能，我们将旧的激光雷达扫描边缘化以进行姿势优化，而不是将激光雷达扫描与全局地图匹配。