Spatially Adaptive Retina-Like Sampling Method for Imaging LiDAR

To mitigate the conflict between imaging quality and speed, a spatially adaptive retina-like sampling method for 3-D imaging Lidar based on time-of-flight method is proposed. The differences between previous retina-like sampling method and the proposed method are described. Sampling points with dense distribution is for the area of interest while sparse distribution is for the area of uninterest, which obtains high imaging quality while consuming much less data acquisition time. Mathematical models of the spatially adaptive retina-like method are developed, and the key parameters are analyzed. To validate the spatially adaptive retina-like sampling method, we perform situational simulations to compare the proposed method with the previous one. Results demonstrate that the proposed method is capable of decreasing data acquisition time without considerable distortion of the interested target. Furthermore, the proposed method is analyzed under different scenes for single and multiple targets. Results illustrate that the proposed method performs better than the previous method.