Semantic map construction based on LIDAR and vision fusion

When Internet of Things (IoT) based unmanned ground vehicles need to perform tasks in complex scenes, traditional geometric maps are difficult to meet the requirements due to insufficient information. Therefore, it is necessary to construct semantic maps. In this paper, we introduce a method to construct 3D semantic maps by integrating LiDAR and camera data. LIDAR is responsible for localization and mapping, while the camera is responsible for high-precision semantic segmentation. Through external parameter calibration between the sensors, a point cloud is projected onto the image, which facilitates the construction of semantic maps by associating each pixel with the corresponding semantic information in the point cloud. Meanwhile, the semantic details in the point cloud are utilized to enhance feature point matching and support back-end SLAM optimization for improved pose estimation. This helps to improve the robustness and accuracy of the whole system. IoT technology can also support remote updating and upgrading of maps so that ground vehicles can access the latest semantic information in a timely manner and adapt to changing environments. In conclusion combining IoT technology with this approach to achieve smarter ground vehicle mission execution and environment awareness.