# LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots

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## Abstract

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## 1. Introduction

- This paper designs and implements a new scheme to optimise the FastSLAM algorithm by means of the Lion Swarm algorithm.
- In the process of optimizing the FastSLAM algorithm through the lion swarm algorithm, the distribution of the particle set after important sampling in the FastSLAM algorithm is achieved through the division of labour between different individuals in the lion swarm optimisation algorithm, so that the particles are distributed in the high likelihood region, and solving the particle weight degradation problem. In addition, to ensure particle diversity, a genetic algorithm is used instead of the lioness movement process in the lion swarm to further increase the particle diversity.
- In this paper, the innovative FastSLAM algorithm is applied to a rescue robot by optimizing the Lion Swarm algorithm, aiming to improve the localization and mapping accuracy of the rescue robot.

## 2. Background of the FastSLAM

- (1)
- Sampling the pose$${x}_{t}^{i}~p({x}_{t}|{x}^{t-1,i},{z}^{t},{u}^{t},{n}^{t})$$
- (2)
- EKF updates the observed landmark estimates.
- (3)
- Importance weight calculation:$${w}_{t}^{i}=\frac{t\mathrm{arg}etdistribition}{propsaldistribition}=\frac{p({x}^{t,i}|{z}^{t},{u}^{t},{n}^{t})}{p\left({x}^{t-1,i}|{z}^{t-1},{u}^{t-1},{n}^{t-1})p\right({x}_{t}^{i}|{x}^{t-1,i},{z}^{t},{u}^{t},{n}^{t})}$$
- (4)
- Re-sampling.
- (5)
- Unknown data associations.
- (6)
- Feature management.

## 3. Lion Swarm Optimization Algorithm

## 4. Lion Swarm Optimization Algorithm Improves FastSLAM

- A.
- Improved Lion position update strategy

- B.
- Reset the Lioness Hunting Method

- C.
- Cub Follow Formula Selection

## 5. Performance Analysis

#### Simulation

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 5.**The algorithm real trajectory: (

**a**) FastSLAM2.0 Simulation Result; (

**b**) GFA-FastSLAM2.0 Simulation Result; (

**c**) LSO-FastSLAM2.0 Simulation Result.

Parameter | Numerical Value | Noise Parameters | Numerical Value |
---|---|---|---|

Robot speed | 3 m/s | Motion noise | 0.3 m/s 1.5° |

Max steering angle | 10° | ||

Maxi steering angular speed | 15°/s | Observation noise | 0.1 m/s 1° |

Wheel spacing | 4 m | ||

Sampling time interval | 0.025 s |

Number of Particles | Algorithm | Mean Localization Accuracy Error/M | RMSE of Road Sign Estimation (m) |
---|---|---|---|

20 | FastSLAM2.0 | 3.0535 | 4.1399 |

GFA-FastSLAM2.0 | 2.9060 | 3.3545 | |

LSO-FastSLAM2.0 | 2.3025 | 2.2837 | |

50 | FastSLAM2.0 | 2.7718 | 3.2106 |

GFA-FastSLAM2.0 | 2.3629 | 2.5990 | |

LSO-FastSLAM2.0 | 2.0470 | 2.2837 | |

80 | FastSLAM2.0 | 2.6843 | 2.9199 |

GFA-FastSLAM2.0 | 1.7504 | 1.9072 | |

LSO-FastSLAM2.0 | 1.2745 | 1.3762 | |

100 | FastSLAM2.0 | 2.5907 | 2.8538 |

GFA-FastSLAM2.0 | 1.3693 | 1.6422 | |

LSO-FastSLAM2.0 | 1.1745 | 1.3279 |

Algorithm | Mean Localization Accuracy Error/m | Variance of Localization Accuracy Error |
---|---|---|

FastSLAM2.0 | 2.7718 | 1.6403 |

GFA-FastSLAM2.0 | 1.4036 | 0.9059 |

LSO-FastSLAM2.0 | 1.1867 | 0.2519 |

Algorithm | RMSE of x-Axis (m) | RMSE of y-Axis (m) | RMSE of Road Sign Estimation (m) |
---|---|---|---|

FastSLAM2.0 | 2.0447 | 2.2676 | 2.9871 |

GFA-FastSLAM2.0 | 1.6015 | 1.1018 | 1.5841 |

LSO-FastSLAM2.0 | 0.6932 | 1.0518 | 1.3383 |

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**MDPI and ACS Style**

Zhu, D.; Ma, Y.; Wang, M.; Yang, J.; Yin, Y.; Liu, S.
LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots. *Sensors* **2022**, *22*, 1297.
https://doi.org/10.3390/s22031297

**AMA Style**

Zhu D, Ma Y, Wang M, Yang J, Yin Y, Liu S.
LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots. *Sensors*. 2022; 22(3):1297.
https://doi.org/10.3390/s22031297

**Chicago/Turabian Style**

Zhu, Daixian, Yinan Ma, Mingbo Wang, Jing Yang, Yichen Yin, and Shulin Liu.
2022. "LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots" *Sensors* 22, no. 3: 1297.
https://doi.org/10.3390/s22031297