A robust approach to elaborately reconstruct the indoor scene with a consumer depth camera is proposed in this paper. In order to ensure the accuracy and completeness of 3D scene model reconstructed from a freely movi...A robust approach to elaborately reconstruct the indoor scene with a consumer depth camera is proposed in this paper. In order to ensure the accuracy and completeness of 3D scene model reconstructed from a freely moving camera, this paper proposes new 3D reconstruction methods, as follows： 1） Depth images are processed with a depth adaptive bilateral filter to effectively improve the image quality; 2） A local-to-global registration with the content-based segmentation is performed, which is more reliable and robust to reduce the visual odometry drifts and registration errors; 3） An adaptive weighted volumetric method is used to fuse the registered data into a global model with sufficient geometrical details. Experimental results demonstrate that our approach increases the robustness and accuracy of the geometric models which were reconstructed from a consumer-grade depth camera.展开更多
Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet parking.Additionally,infrastructure-based cooperative driving systems have become a means t...Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet parking.Additionally,infrastructure-based cooperative driving systems have become a means to realizing intelligent driving.In this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking lots.In the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of data.Meanwhile,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass deployments.Hence,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth data.Finally,a complete hardware system was built to verify the feasibility of our solution in a real-world environment.Experiments in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.展开更多
基金supported by the National Key Technologies R&D Program(No.2016YFB0502002)National Natural Science Foundation of China(Nos.61472419,61421004 and 61572499)
文摘A robust approach to elaborately reconstruct the indoor scene with a consumer depth camera is proposed in this paper. In order to ensure the accuracy and completeness of 3D scene model reconstructed from a freely moving camera, this paper proposes new 3D reconstruction methods, as follows： 1） Depth images are processed with a depth adaptive bilateral filter to effectively improve the image quality; 2） A local-to-global registration with the content-based segmentation is performed, which is more reliable and robust to reduce the visual odometry drifts and registration errors; 3） An adaptive weighted volumetric method is used to fuse the registered data into a global model with sufficient geometrical details. Experimental results demonstrate that our approach increases the robustness and accuracy of the geometric models which were reconstructed from a consumer-grade depth camera.
基金the National Natural Science Foundation of China(No.62173228)。
文摘Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet parking.Additionally,infrastructure-based cooperative driving systems have become a means to realizing intelligent driving.In this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking lots.In the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of data.Meanwhile,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass deployments.Hence,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth data.Finally,a complete hardware system was built to verify the feasibility of our solution in a real-world environment.Experiments in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.
