2026-10-10T22:41:49Z https://www.scidb.cn/oai

10.57760/sciencedb.11587 2024-12-31T09:07:40Z

2024-12-31 Multi-source Heterogeneous sensor fusion mapping method for UAV doi:10.57760/sciencedb.11587 en The video is the dynamic process of experimental data in the paper, and compared to static images, it can better reflect the changing state of experimental data (such as the representation of critical occupancy state on the map in smoke scenes). The video consists of a running dataset, mileage calculation method, and the fusion grid map algorithm proposed in this article. Among them, Garden and Smoke scenes from the NTU4DRADLM dataset were used, which includes IMU, LIDAR point cloud (Livox format), imaging radar point cloud (sensor-msgs/PointCloud), thermal imaging, and visible light cameras. This article uses IMU and point clouds from two radars (with two input sources and different radar configurations). Project Address: https://github.com/junzhang2016/NTU4DRadLM . Due to the difference between the point cloud format and the commonly used sensor-msgs/PointCloud2, refer to this open source project to convert the Customs Message to sensor-msgs/PointCloud2. (Simultaneously publish the translated point cloud for visualization). Project Address: https://github.com/jianfee/livox_repub  occupancy grid map; Inverse sensor model; cale transformation; sensor fusion; lidar; imaging radar Wang Xiaotian PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank