LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

TLDR

The authors introduce LIO‑SAM, a tightly‑coupled lidar‑inertial odometry framework that delivers accurate, real‑time trajectory estimation and map building for mobile robots. LIO‑SAM models lidar‑inertial odometry as a factor graph, using IMU pre‑integration to de‑skew scans, marginalizing old scans, and employing local scan matching with keyframes and a sliding‑window strategy to integrate relative and absolute measurements, including loop closures. The approach achieves real‑time performance gains through local scan matching, selective keyframe insertion, and a sliding‑window strategy, and is validated on diverse datasets from three platforms.

Abstract

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.