Camera Depth Map Research Articles

Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera

Robot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSense camera data. By using this method, any current single-camera detection system can be upgraded online. Without changing the original system, the depth information of the original monocular camera can be obtained simply, and the transition from 2D detection to 3D detection can be realized. In order to verify the effectiveness of the proposed method, a hardware system was constructed using the Micro-vision RS-A14K-GC8 industrial camera and the Intel RealSense D415 depth camera, and the depth map fitting algorithm proposed in this paper was used to test the system. The results show that, except for a few depth-missing areas, the results of other areas with depth are still good, which can basically describe the distance difference between the target and the camera. In addition, in order to verify the scalability of the method, a new hardware system was constructed with different cameras, and images were collected in a complex farmland environment. The generated depth map was good, which could basically describe the distance difference between the target and the camera.

Large-scale outdoor scene reconstruction and correction with vision

We provide the theory and the system needed to create large-scale dense reconstructions for mobile-robotics applications: this stands in contrast to the object-centric reconstructions dominant in the literature. Our BOR2G system fuses data from multiple sensor modalities (cameras, lidars, or both) and regularizes the resulting 3D model. We use a compressed 3D data structure, which allows us to operate over a large scale. In addition, because of the paucity of surface observations by the camera and lidar sensors, we regularize over both two (camera depth maps) and three dimensions (voxel grid) to provide a local contextual prior for the reconstruction. Our regularizer reduces the median error between 27% and 36% in 7.3 km of dense reconstructions with a median accuracy between 4 and 8 cm. Our pipeline does not end with regularization. We take the unusual step to apply a learned correction mechanism that takes the global context of the reconstruction and adjusts the constructed mesh, addressing errors that are pathological to the first-pass camera-derived reconstruction. We evaluate our system using the Stanford Burghers of Calais, Imperial College ICL-NUIM, Oxford Broad Street (released with this paper), and the KITTI datasets. These latter datasets see us operating at a combined scale and accuracy not seen in the literature. We provide statistics for the metric errors in all surfaces created compared with those measured with 3D lidar as ground truth. We demonstrate our system in practice by reconstructing the inside of the EUROfusion Joint European Torus (JET) fusion reactor, located at the Culham Centre for Fusion Energy (UK Atomic Energy Authority) in Oxfordshire.