Fusion of sparse LIDAR and uncertainty-based 3D vision for accurate depth estimation with Bayesian Kriging

Abstract

Accurate 3D perception is vital for the localization of mobile robots and autonomous vehicles. Sparse 3D LIDAR scanners and 3D vision sensors (stereo and RGB-D cameras) are commonly used environment perception sensors. The former could provide accurate but sparse-range data, while the latter is dense but full of uncertainty. Since the acquired depth map by each sensor alone could not accurately perceive complex surrounding environment, an innovative probabilistic fusion model based on Bayesian Kriging (BK) is proposed for dense high-precision depth map estimation, to fully utilize the complementary characteristics of the two distinct sensors. Combing the geometric piece-wise planar hypotheses of the scene, the noisy disparity map from the 3D vision sensor is segmented into multiple small planes. In each segmented plane, the BK-based fusion estimator is founded to predict depth for each pixel with sparse scattered LIDAR points. In particular, we focus on analyzing the spatial variation prior and correlation between these two types of data, based on regionalized variable theory. Experiments on various scenes of KITTI stereo datasets are carried out to demonstrate the effectiveness of our algorithm, and validate the superiority compared with existing fusion methods.