An Investigation of Interpolation Techniques to Generate 2D Intensity Image From LIDAR Data

Light detection and ranging (LIDAR) has become a part and parcel of ongoing research in autonomous vehicles. LIDAR efficiently captures data during day and night alike; yet, data accuracy is affected in altered weather conditions. LIDAR data fusion with sensors, such as color camera, hyperspectral camera, and RADAR, proves to be a viable solution to improve the quality of data and add spectral information. LIDAR 3-D point cloud containing intensity data are transformed to 2-D intensity images for the said purpose. LIDAR produces large point cloud, but, while generating images for limited field of view, data sparsity results in poor quality images. Moreover, 3-D to 2-D data transformation also involves data reduction, which further deteriorates the quality of images. This paper focuses on generating intensity images from LIDAR data using interpolation techniques, including bi-linear, natural neighbor, bi-cubic, kriging, inverse distance, and weighted and nearest neighbor interpolation. The main focus is to test the suitability of interpolation methods for 2-D image generation, and analyze the quality of the generated 2-D image. Image similarity metrics, such as root mean square error, normalized least square error, peak signal-to-noise ratio, correlation, difference entropy, mutual information, and structural similarity index measurement, are utilized for camera and LIDAR image matching, and their ability to compare images from heterogeneous sensors is also analyzed. Generated images can further be used for data fusion purpose. Images generated using LIDAR points have a relevant distance matrix as well, which can be used to find the distance of any given pixel from the image. In addiiton, the accuracy of interpolated distance data is evaluated as well by comparing it with the original distance values of traffic cones placed in front of vehicle. Results show that the inverse distance weighted interpolation outperforms other selected methods in 2-D image quality, and images from nearest neighbor appear brighter subjectively.