Tsinghua University

Zhenhong Zou

Jun Li

Zhiwei Li

Xinyu Zhang

Li Wang

Yi Huang

Huaping Liu

A point cloud intensity completion method and system based on semantic segmentation are provided. The point cloud intensity completion method includes: acquiring an RGB image and point cloud data of a road surface synchronously by a photographic camera and a lidar; performing spatial transformation on the point cloud data by using a conversion matrix to generate a two-dimensional reflection intensity projection map and a two-dimensional depth projection map; performing reflection intensity completion on the RGB image and the two-dimensional reflection intensity projection map to obtain a single-channel reflection intensity projection map; performing depth completion on the RGB image and the two-dimensional depth projection map to obtain a single-channel depth projection map; and performing coarse-grained completion on the RGB image, the single-channel reflection intensity projection map and the single-channel depth projection map to obtain a two-dimensional coarse-grained reflectance intensity projection map.

Point cloud intensity completion method and system based on semantic segmentation

An adaptive multi-sensor data fusion method based on mutual information includes: receiving an RGB image of a road surface collected by a camera; receiving point cloud data of the road surface collected synchronously by LIDAR; preprocessing the point cloud data to obtain dense point cloud data; and inputting the RGB image and the dense point cloud data into a pre-established and well-trained fusion network, to output data fusion results. The fusion network is configured to calculate mutual information of a feature tensor and an expected feature of input data, assign fusion weights of the input data according to the mutual information, and then output the data fusion results according to the fusion weights. In the new method, such information theory tool as mutual information is introduced, to calculate the correlation between the extracted feature of the input data and the expected feature of the fusion network.

Adaptive multi-sensor data fusion method and system based on mutual information

A lane detection method based on vision and lidar multi-level fusion includes: calibrating obtained point cloud data and an obtained video image; constructing a point cloud clustering model by fusing height information, reflection intensity information of the point cloud data, and RGB information of the video image, obtaining point clouds of a road based on the point cloud clustering model, and obtaining a lane surface as a first lane candidate region by performing least square fitting on the point clouds; obtaining four-channel road information by fusing the reflection intensity information of the point cloud data and the RGB information of the video image, inputting the four-channel road information into the semantic segmentation network 3D-LaneNet, and outputting an image of a second lane candidate region; and fusing the first lane candidate region and the second lane candidate region, and combining the two lane candidate regions into a final lane region.

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Beijing, China

Zhenhong Zou