TY - GEN
T1 - LIO-SAM
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
AU - Shan, Tixiao
AU - Englot, Brendan
AU - Meyers, Drew
AU - Wang, Wei
AU - Ratti, Carlo
AU - Rus, Daniela
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097746837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097746837&partnerID=8YFLogxK
U2 - 10.1109/IROS45743.2020.9341176
DO - 10.1109/IROS45743.2020.9341176
M3 - Conference contribution
AN - SCOPUS:85097746837
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 5135
EP - 5142
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -