Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and lidar intensity measurements that are robust to ambient illumination. However, the systems still pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse.
From a research paper by Weichen Dai, et al.
In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment.
We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth.
Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.
To train this network, a vehicle carrying a Velodyne HDL64 LiDAR was used to collect data in two 500 × 500 m2 areas. Then, we rectified the raw intensity by Lambertian reflection model and fused sparse intensity maps from a sequence of data frames to build an intensity dataset.
For the complete paper CLICK HERE.
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