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

TL;DR

LIO-SAM is a real-time, highly accurate lidar-inertial odometry framework that uses factor graphs and local scan matching to improve mobile robot trajectory estimation and mapping.

Contribution

It introduces a tightly-coupled lidar-inertial odometry system using factor graphs with efficient local scan matching and keyframe management for real-time performance.

Findings

Achieves high accuracy in diverse environments

Operates in real-time with efficient marginalization

Outperforms previous methods in trajectory estimation

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.