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LIDAR-Inertial Real-Time State Estimator with Rod-Shaped and Planar Feature
| Content Provider | MDPI |
|---|---|
| Author | Liu, Hong Pan, Shuguo Gao, Wang Ma, Chun Jia, Fengshuo Lu, Xinyu |
| Copyright Year | 2022 |
| Description | State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud feature selection for LIDAR-inertial tightly coupled systems is proposed. In the front-end, a point cloud registration is carried out after marking rod-shaped and planar feature information which is different from the existing LIDAR and inertial measurement unit (IMU) integration scheme. This preprocessing method subsequently reduces the outliers. IMU pre-integration outputs high-frequency result and is used to provide the initial value for LIDAR solution. In the scan-to-map module, a computationally efficient graph optimization framework is applied. Moreover, the LIDAR odometry further constrains the IMU states. In the back-end, the optimization based on sliding-window incorporates the LIDAR-inertial measurement and loop closure global constraints to reduce the cumulative error. Combining the front-end and back-end, we propose the low drift and high real-time LIDAR-inertial positioning system. Furthermore, we conducted an exhaustive comparison in open data sequences and real-word experiments. The proposed system outperforms much higher positioning accuracy than the state-of-the-art methods in various scenarios. Compared with the LIO-SAM, the absolute trajectory error (ATE) average RMSE (Root Mean Square Error) in this study increases by 64.45% in M2DGR street dataset (street_01, 04, 07, 10) and 24.85% in our actual scene datasets. In the most time-consuming mapping module of each system, our system runtime can also be significantly reduced due to the front-end preprocessing and back-end graph model. |
| Starting Page | 4031 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14164031 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-18 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Tightly-coupled Integration Lidar-inertial Slam Rod-shaped and Planar Feature Sliding-window Graph Optimization Framework |
| Content Type | Text |
| Resource Type | Article |
