Upsampling of 16-channel LiDAR depth data using weighted median filter

Three-dimensional (3D) LiDAR is widely used because it can accurately measure 360° 3D depth. One of the most widely used sensors to this end is VLP-16, a 16-channel LiDAR manufactured by Velodyne. This 16-channel LiDAR provides dense 3D depth data in the horizontal direction. However, in the vertical direction, it provides only sparse depth data owing to the LiDAR architecture. We propose an upsampling method to obtain a dense 3D depth map from a 16-channel LiDAR by applying an edge-aware filter to the LiDAR data. A camera-LiDAR sensor system composed of a 16-channel LiDAR and six vision cameras is used for this purpose. First, the LiDAR depth data are projected onto the camera image coordinates to generate a 2.5D depth map. Next, bilinear interpolation and a weighted median filter are applied to upsample the original depth map. Compared with the original LiDAR data, we can generate an extremely dense depth map by using the proposed method. The results of quantitative analysis show that the proposed method can dramatically increase the number of 3D depth points and reduce the depth reconstruction error.