Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion

Abstract

Free space detection is crucial to autonomous vehicles while existing works are not entirely satisfactory. As cameras have many advantages on environment perception, a stereo vision-based robust free space detection method is proposed which mainly depends on geometry information and Gaussian process regression. In this work, in order to improve the performance by exploiting multiple source information, we apply Bayesian framework and conditional random field inference to fuse the multimodal information including 2-D image and 3-D point geometric information. Particularly, a Bayesian framework is used for multiple feature fusion to provide a normalized and flexible output. Gaussian process regression is used to automatically and incrementally regress the data, resulting enhanced performance. Finally, conditional random field with color and geometry constrains is applied to make the result more robust. In order to evaluate the proposed method, quantitative experiments on popular KITTI-road data set and qualitative experiments on our own campus data set are tested. The results show satisfactory and inspiring performance compared to the outstanding works and even are competitive to some relevant Lidar-based methods.

A Road Environment Prediction System for Intelligent Vehicle

“…Xiao et al [21] have presented a Bayesian framework and conditional random field to fuse the multiple features that includes 2D image and 3D point cloud geometric information. Besides, a Gaussian process regression is employed to enhance performance.…”

Multimodal fusion for robotics

“…This data set provides a new benchmark for research purposes. In another work, 13 a stereovision-based robust free space detection method, which mainly depends on geometry information and Gaussian process regression, is proposed. The authors apply Bayesian framework and conditional random field inference to fuse the multimodal information including two-dimensional image and three-dimensional point geometric information.…”

Moving-Vehicle Identification Based on Hierarchical Detection Algorithm

“…The vehicle detection algorithms based on machine vision mainly include the method based on motion information detection [8][9][10], the method based on prior knowledge detection [11][12][13], the detection method based on stereo information [14][15][16] and the detection method based on machine learning [17][18][19]. Compared with other detection methods based on machine vision, detection methods based on machine learning are more outstanding in recognition performance and robustness [20].…”