  According to the requirement of general static obstacle detection, this paper
proposes a compact vectorization representation approach of local static
environments for unmanned ground vehicles. At first, by fusing the data of
LiDAR and IMU, high-frequency pose information is obtained. Then, through the
two-dimensional (2D) obstacle points generation, the process of grid map
maintenance with a fixed size is proposed. Finally, the local static
environment is described via multiple convex polygons, which is realized
throungh the double threshold-based boundary simplification and the convex
polygon segmentation. Our proposed approach has been applied in a practical
driverless project in the park, and the qualitative experimental results on
typical scenes verify the effectiveness and robustness. In addition, the
quantitative evaluation shows the superior performance on making use of fewer
number of points information (decreased by about 60%) to represent the local
static environment compared with the traditional grid map-based methods.
Furthermore, the performance of running time (15ms) shows that the proposed
approach can be used for real-time local static environment perception. The
corresponding code can be accessed at https://github.com/ghm0819/cvr_lse.
