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

By Tixiao Shan, Brendan Englot, Drew Meyers, Wei Wang, Carlo Ratti, Daniela Rus. IEEE/RJS International Conference on Intelligent RObots and Systems, 2020

2,021 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e49902d212374c81b48d23c3cac1cee6e2339edd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fu Zhang
2. Jiarong Lin
3. Chen Wang
4. Cyrill Stachniss
5. Lihua Xie
6. Han Wang
7. Jens Behley
8. Ignacio Vizzo

In Computer Vision and Pattern Recognition

1. Xinggang Wang
2. Wenyu Liu
3. Chen Wang
4. Fu Zhang
5. Qian Zhang
6. Jiarong Lin
7. Lihua Xie
8. Han Wang

Citations in the corpus

• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, methodology): “LIO-SAM [13], which bounds computational complexity by introducing a sliding window of lidar keyframes, utilizes a factor graph for joint IMU and lidar constraint optimization.”
• FAST-LIO2: Fast Direct LiDAR-Inertial Odometry (2021, methodology): “…the local map in the above works is usually small to obtain real-time performance, the odometry drifts quickly, necessitating a low-rate mapping process, such as map refining (LINS [31]), sliding window joint optimization (LILI-OM [17] and LIOM [29]) and factor graph smoothing [32] (LIO-SAM [30]).”
• KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way (2022, methodology): “See https://www.ieee.org/publications/rights/index.html for more information. profiles with the same system configuration, and (iii) provides an effective solution to motion distortion without relying on IMUs or wheel odometers.”
• F-LOAM : Fast LiDAR Odometry and Mapping (2021, methodology): “Shan et al propose a fusion method with IMU and GPS named LIO-SLAM [13].”
• MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction (2022, methodology): “Conventionally HD map is constructed offline with SLAM-based methods (Zhang & Singh, 2014; Shan & Englot, 2018; Shan et al., 2020), incurring complicated pipeline and high maintaining cost.”