# R-LIO: Rotating Lidar Inertial Odometry and Mapping

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Rotating Lidar Inertial Odometry and Mapping

#### 3.1. A System Overview

#### 3.2. B Motion Distortion Compensation

#### 3.3. C Self-Adaptive Gird Divide

#### 3.4. D Lidar Inertial Odometry

#### 3.5. E Lidar Mapping

#### 3.6. F Loop Closure Detection

## 4. Experiments

#### 4.1. KITTI Public Dataset

#### 4.2. Other Public Dataset

#### 4.2.1. Park

#### 4.2.2. Campus

#### 4.2.3. Handheld

#### 4.2.4. Jackal

#### 4.3. Private Dataset

#### 4.3.1. Corridor

#### 4.3.2. Garage

#### 4.3.3. Mine

#### 4.3.4. Tunnel

#### 4.3.5. Forest

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 7.**The comparison of estimated trajectories after applying R-LIO-odom and LOAM algorithms on KITTI dataset.

**Figure 15.**Local mapping results of different algorithms in corridor dataset at the beginning and end positions.

**Figure 19.**Local mapping results of different algorithms in forest dataset at the beginning and end positions. (Left: Plane direction error, Right: Elevation direction error).

Dataset | Scans | Trajectory Length (m) | Lidar Rate (Hz) | IMU Rate (Hz) | Time (s) |
---|---|---|---|---|---|

KITTI05 | 2762 | 2223 | 10 | 100 | 288 |

KITTI07 | 1106 | 695 | 10 | 100 | 115 |

Park | 5467 | 650.6 | 10 | 500 | 560 |

Campus | 4043 | 566.0 | 10 | 500 | 407 |

Handheld | 2969 | 391.4 | 10 | 500 | 300 |

Jackal | 3966 | 427.0 | 10 | 500 | 400 |

Corridor | 2540 | 148.3 | 10 | 400 | 256 |

Garage | 3417 | 370.3 | 10 | 400 | 344 |

Mine | 4878 | 490.2 | 10 | 400 | 491 |

Tunnel | 5266 | 537.1 | 10 | 400 | 534 |

Forest | 8387 | 1105 | 10 | 400 | 845 |

KITTI Dataset | Error Type | LOAM | R-LIO-Odom |
---|---|---|---|

Seq.05 | RMSE w.r.t GPS(m) | 1.78 | 0.81 |

Seq.07 | RMSE w.r.t GPS(m) | 0.55 | 0.12 |

Dataset | Error Type | ALOAM | LIO-SAM-Odom | FAST-LIO2 | Faster-LIO | R-LIO-Odom |
---|---|---|---|---|---|---|

Park | RMSE w.r.t GPS(m) | 10.799 | 1.294 | 1.532 | 1.537 | 1.391 |

Campus | RMSE w.r.t GPS (m) | 59.890 | 0.648 | 0.650 | 0.657 | 0.671 |

Handheld | RMSE w.r.t GPS (m) | 63.779 | 0.858 | Fail | 3.808 | 0.669 |

Jackal | RMSE w.r.t GPS (m) | 23.290 | 0.377 | 0.246 | 0.340 | 0.297 |

Dataset | Error Type | ALOAM | LIO-SAM-Odom | FAST-LIO2 | Faster-LIO | R-LIO-Odom |
---|---|---|---|---|---|---|

Corridor | End-to-end translation error (m) | 1.968 | 0.063 | 1.220 | 0.632 | 0.041 |

Parking | End-to-end translation error (m) | 12.199 | 0.057 | 0.053 | 0.028 | 0.037 |

Mine | End-to-end translation error (m) | 74.366 | 0.124 | 10.412 | 0.083 | 0.041 |

Various Methods | End-to-End Translation Error (m) No Rotating Data | End-to-End Translation Error (m) Rotating Data |
---|---|---|

ALOAM | 168.133 | Fail |

LIO-SAM-odom | 11.775 | Fail |

LIO-SAM | 0.641 | Fail |

Fast-LIO2 | 10.279 | Fail |

Faster-LIO | 6.744 | Fail |

R-LIO-odom | 3.563 | 0.062 |

R-LIO | 0.053 | 0.046 |

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**MDPI and ACS Style**

Chen, K.; Zhan, K.; Pang, F.; Yang, X.; Zhang, D.
R-LIO: Rotating Lidar Inertial Odometry and Mapping. *Sustainability* **2022**, *14*, 10833.
https://doi.org/10.3390/su141710833

**AMA Style**

Chen K, Zhan K, Pang F, Yang X, Zhang D.
R-LIO: Rotating Lidar Inertial Odometry and Mapping. *Sustainability*. 2022; 14(17):10833.
https://doi.org/10.3390/su141710833

**Chicago/Turabian Style**

Chen, Kai, Kai Zhan, Fan Pang, Xiaocong Yang, and Da Zhang.
2022. "R-LIO: Rotating Lidar Inertial Odometry and Mapping" *Sustainability* 14, no. 17: 10833.
https://doi.org/10.3390/su141710833