# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
###### Abstract  
In this paper, a lightweight, high-definition mapping method is proposed for autonomous driving to address the drawbacks of traditional mapping methods, such as high cost, low efficiency, and slow update frequency. The proposed method is based on multi-source data fusion perception and involves generating local semantic maps (LSMs) using multi-sensor fusion on a vehicle and uploading multiple LSMs of the same road section, obtained through crowdsourcing, to a cloud server. An improved, two-stage semantic alignment algorithm, based on the semantic generalized iterative closest point (GICP), was then used to optimize the multi-trajectories pose on the cloud. Finally, an improved density clustering algorithm was proposed to instantiate the aligned semantic elements and generate vector semantic maps to improve mapping efficiency. Experimental results demonstrated the accuracy of the proposed method, with a horizontal error within 20 cm, a vertical error within 50 cm, and an average map size of 40 Kb/Km. The proposed method meets the requirements of being high definition, low cost, lightweight, robust, and up-to-date for autonomous driving.  
c 2023 13 3264 10.3390/app13053264 10.3390/app13053264 10.  
Finally, the SICP algorithm improves on the ICP algorithm by adding semantic prior information, which results in higher alignment accuracy and faster convergence speed.  
After the semantic alignment of multiple trajectories, a lightweight vector semantic map can be obtained by merging maps on the cloud server--a process known as semantic aggregation. Two different semantic aggregation methods have been proposed. [PERSON] et al. [14] used rich, high-precision sensors to build local segment maps (LSMs) of the vehicle, and only semantic element aggregation was needed on the cloud. Their semantic aggregation method generates a grid map on the cloud and divides the LSM into 0.1 m \(\times\) 0.1 m \(\times\) 0.1 m small grids, each containing the location, the semantic element type, and the number of each semantic element type. The global map is merged in the cloud according to the grid of the same resolution, and the semantic element type with the highest score in each grid is used as the semantic type after aggregation. This method is more suitable for the local map generated by sensors with high accuracy and low noise tolerance. In contrast, [PERSON] et al. [15] used crowdsourced data from commercial sensors and fused multi-period vehicle terminal maps in the cloud. The core of their alignment algorithm is to extract the landmarks observed in the keyframe of the sub-map common view, match the features, calculate the relative pose as a closed-loop constraint, and finally, construct the vector map through semantic map reconstruction. However, this method is computationally expensive and inefficient, and it requires a large storage bandwidth.  
In summary, this paper proposes an improved semantic alignment algorithm and semantic clustering algorithm for lightweight mapping by merging LSMs on the cloud server. This low-cost, crowdsourced semantic mapping solution has the potential to reduce mapping costs and promote the development and application of autonomous driving technology.