Abstract
This paper presents a global map smoothing method to reduce the adverse impact of localization errors on the map. The errors along the direction of the ground normal vector will lead to map distortion, for example an undulating or stepped ground in the map. This type of error is difficult to be eliminated in visual or LiDAR odometry technically. The flat ground constraints are usually added to the odometry stage of SLAM to figure out above problem in structured scenes like urban road or indoor environments. However, it does not fit most of unstructured scenarios, such as the desert or the forest. Instead of solving this type of localization error, a global map smoothing method is proposed in our paper to improve the mapping accuracy. The matching method is applied between two a local map and the corresponding submap extracted from the global map. The pose correction is computed and then is applied to correct the local map. Finally, the corrected local map is fused into the global map. Demonstration is performed in our experimental environment and open source dataset, respectively.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. U21B6001), and Center-initialized Research Project of Zhejiang Lab (No. 2021NB0AL02).
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Zhou, Y., Zhu, S., Li, Y. (2023). Map Smoothing Method for Map Distortion with Localization Error. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_41
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DOI: https://doi.org/10.1007/978-981-99-6483-3_41
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