Tightly Coupled 3D Lidar Inertial Odometry and Mapping

By Haoyang Ye, Yuying Chen, Ming Liu. IEEE International Conference on Robotics and Automation, 2019

506 citations recorded by the source corpus.

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidarIMU odometry (LIO) can perform well with considerable drifts after long-term experiment, even in challenging cases where the lidar measurement can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/aa9c1f9eb3d9f2c0177fc65e37246d51515528f5

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. Daniela Rus
4. Carlo Ratti
5. Wei Xu
6. Brendan Englot
7. Tixiao Shan
8. Dongjiao He

In Electrical and Electronic Engineering

1. Fu Zhang
2. Daniel Gehrig
3. Carlo Ratti
4. Daniela Rus
5. Brendan Englot
6. Tixiao Shan
7. Jiarong Lin
8. Davide Scaramuzza

In Environmental Engineering

1. Fu Zhang
2. Wei Xu
3. Carlo Ratti
4. Daniela Rus
5. Brendan Englot
6. Tixiao Shan
7. Jiarong Lin
8. Ming Liu

Citations in the corpus

• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “A tightly-coupled lidar inertial odometry and mapping framework, LIOM, is introduced in [17].”
• FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter (2020, background): “Recently, Ye et al [18] propose the LIOM package which uses a similar graph optimization but is based on edge and plane features.”
• FAST-LIO2: Fast Direct LiDAR-Inertial Odometry (2021, methodology): “More specifically, LIOM [29] presents a tightly-coupled LiDAR inertial fusion method where the IMU preintegrations are introduced into the odometry .”
• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, methodology): “Because LIO-mapping is designed to optimize all measurements, real-time performance is not achieved.”
• DSEC: A Stereo Event Camera Dataset for Driving Scenarios (2021, methodology): “To improve the ground truth density, LIO-Mapping [45] computes the pose for each scan of the lidar.”
• LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation (2019, methodology): “[5] established LIO-mapping (for brevity, termed LIOM in the followings) which is also based on graph optimization but with a novel rotation-constrained mapping method to optimize the final poses and maps.”