...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Optical Engineering >Fusion of sparse LIDAR and uncertainty-based 3D vision for accurate depth estimation with Bayesian Kriging
【24h】

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

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

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

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. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号